VRAM & GPU cost calculator: live GPU rental prices for any Hugging Face model

README.md

@@ -1,10 +1,1545 @@
 ---
-title: Vram And Gpu Cost Calculator
-emoji: 📉
-colorFrom: red
-colorTo: gray
+title: VRAM & GPU Cost Calculator
+emoji: ⚡
+colorFrom: blue
+colorTo: indigo
 sdk: static
-pinned: false
+pinned: true
+license: mit
+short_description: VRAM any HF model needs + live GPU rental prices
+tags:
+  - vram
+  - gpu
+  - gpu-prices
+  - calculator
+  - inference
+  - fine-tuning
+  - llm
+  - cloud-gpu
+models:
+  - Qwen/Qwen3-0.6B
+  - google/gemma-4-26B-A4B-it
+  - Qwen/Qwen3-8B
+  - BAAI/bge-large-en-v1.5
+  - Qwen/Qwen3-VL-8B-Instruct
+  - Qwen/Qwen3-4B
+  - google/gemma-4-31B-it
+  - Qwen/Qwen3-Embedding-0.6B
+  - Qwen/Qwen3.5-9B
+  - Qwen/Qwen2.5-7B-Instruct
+  - Qwen/Qwen3.5-4B
+  - meta-llama/Llama-3.2-1B-Instruct
+  - intfloat/multilingual-e5-large
+  - Qwen/Qwen3.8-27B
+  - Qwen/Qwen2.5-1.5B-Instruct
+  - openai/whisper-large-v3-turbo
+  - zai-org/GLM-5.3-Flash
+  - RadixArk/Kimi-K3-DSpark
+  - meta-llama/Llama-3.1-8B-Instruct
+  - openai/gpt-oss-20b
+  - unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF
+  - Qwen/Qwen2.5-VL-7B-Instruct
+  - nvidia/Qwen3.6-35B-A3B-NVFP4
+  - ornith-ai/Ornith-1.5-9B-GGUF
+  - Qwen/Qwen3-14B
+  - dphn/dolphin-2.9.1-yi-1.5-34b
+  - Qwen/Qwen3.8-27B-FP8
+  - deepseek-ai/DeepSeek-V4-Flash-0731
+  - Qwen/Qwen3.6-35B-A3B-FP8
+  - google/gemma-4-E4B-it
+  - stabilityai/stable-diffusion-xl-base-1.0
+  - deepseek-ai/DeepSeek-V3.2
+  - prism-ml/Ternary-Bonsai-2-27B-gguf
+  - Qwen/Qwen2.5-3B-Instruct
+  - openai/gpt-oss-120b
+  - Qwen/Qwen3.5-2B
+  - openai/whisper-large-v3
+  - cdiamond/Qwen3.8-27B-iMatrix-NVFP4-MTP-GGUF
+  - ornith-ai/Ornith-1.5-35B-A3B-GGUF
+  - Qwen/Qwen3-32B
+  - Qwen/Qwen3-4B-Instruct-2507
+  - google/embeddinggemma-300m
+  - Qwen/Qwen3.6-35B-A3B
+  - ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF
+  - openai/whisper-small
+  - Qwen/Qwen3-VL-4B-Instruct
+  - google/gemma-3-1b-it
+  - Qwen/Qwen3-1.7B
+  - Qwen/Qwen3-14B-AWQ
+  - google/gemma-4-E2B-it
+  - Qwen/Qwen3-VL-2B-Instruct
+  - Qwen/Qwen2.5-7B-Instruct-AWQ
+  - MahmoudAshraf/mms-300m-1130-forced-aligner
+  - Qwen/Qwen3.5-0.8B
+  - CMSManhattan/JiRackUltra_1b
+  - ornith-ai/Ornith-1.0-9B-GGUF
+  - Qwen/Qwen3-Embedding-8B
+  - Qwen/Qwen3.6-27B-FP8
+  - BAAI/bge-reranker-large
+  - Qwen/Qwen3-8B-AWQ
+  - Qwen/Qwen3.6-27B
+  - Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
+  - Qwen/Qwen2.5-VL-3B-Instruct
+  - vikhyatk/moondream2
+  - Qwen/Qwen2.5-Coder-7B-Instruct
+  - deepseek-ai/DeepSeek-OCR
+  - mixedbread-ai/mxbai-embed-large-v1
+  - jinaai/jina-embeddings-v3
+  - nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
+  - mistralai/Voxtral-Mini-4B-Realtime-2602
+  - Qwen/Qwen3.5-27B
+  - handy-computer/nemotron-3.5-asr-streaming-0.6b-gguf
+  - ggml-org/Qwen3.8-27B-GGUF
+  - datalab-to/chandra-ocr-2
+  - Qwen/Qwen2.5-32B-Instruct
+  - Qwen/Qwen3-Embedding-4B
+  - handy-computer/parakeet-unified-en-0.6b-gguf
+  - farbodtavakkoli/OTel-2.0-LLM-31B-IT
+  - Qwen/Qwen3-VL-8B-Instruct-FP8
+  - peculiar-ragdoll/Tiel-Coder-35B-A3B-GGUF-MTP
+  - Qwen/Qwen2.5-14B-Instruct-AWQ
+  - google/gemma-4-12B-it
+  - Qwen/Qwen2.5-VL-7B-Instruct-AWQ
+  - Qwen/Qwen3.8-Flash-Next
+  - ornith-ai/Ornith-1.0-35B-GGUF
+  - stable-diffusion-v1-5/stable-diffusion-v1-5
+  - unsloth/gemma-4-12b-it-GGUF
+  - Qwen/Qwen2-VL-7B-Instruct-AWQ
+  - Qwen/Qwen2.5-Coder-14B-Instruct
+  - mistralai/Mistral-7B-Instruct-v0.2
+  - zai-org/GLM-4.7-Flash
+  - meta-llama/Llama-3.2-3B-Instruct
+  - zai-org/GLM-5.3
+  - intfloat/multilingual-e5-large-instruct
+  - autotrust/JEV-27B-VL
+  - ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF
+  - nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4
+  - Alibaba-NLP/gte-multilingual-base
+  - Qwen/Qwen3-VL-Embedding-8B
+  - facebook/w2v-bert-2.0
+  - Qwen/Qwen3.5-35B-A3B
+  - peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF
+  - ornith-ai/Ornith-1.0-35B
+  - k2-fsa/OmniVoice
+  - datalab-to/surya-ocr-2
+  - Qwen/Qwen3-30B-A3B
+  - nvidia/Gemma-4-31B-IT-NVFP4
+  - deepseek-ai/DeepSeek-V3-0324
+  - nvidia/Qwen2.5-VL-7B-Instruct-NVFP4
+  - deepseek-ai/DeepSeek-V3
+  - llava-hf/llava-1.5-7b-hf
+  - Qwen/Qwen3.5-35B-A3B-FP8
+  - Qwen/Qwen2.5-14B-Instruct
+  - unsloth/Qwen3.8-Flash-Next-GGUF
+  - baidu/Unlimited-OCR
+  - Qwen/Qwen2-VL-2B-Instruct
+  - unsloth/Qwen3.6-35B-A3B-GGUF
+  - TinyLlama/TinyLlama-1.1B-Chat-v1.0
+  - nvidia/nemotron-3.5-asr-streaming-0.6b
+  - HuggingFaceTB/SmolVLM2-500M-Video-Instruct
+  - deepseek-ai/DeepSeek-V4.1-Flash
+  - Alibaba-NLP/gte-large-en-v1.5
+  - Qwen/Qwen3-ASR-1.7B
+  - openbmb/MiniCPM5-2B
+  - antirez/deepseek-v4-gguf
+  - moonshotai/Kimi-K3
+  - google/gemma-3-4b-it
+  - ornith-ai/Ornith-1.5-397B-GGUF
+  - unsloth/Qwen3.5-9B-GGUF
+  - bigscience/bloomz-560m
+  - gigant/romanian-wav2vec2
+  - unsloth/Qwen3.5-4B-GGUF
+  - Qwen/Qwen2.5-32B-Instruct-AWQ
+  - intfloat/e5-large-v2
+  - Qwen/Qwen3-VL-Embedding-2B
+  - nomic-ai/nomic-embed-text-v2-moe
+  - deepseek-ai/DeepSeek-R1
+  - openai-community/gpt2-large
+  - dots-studio/dots.ocr
+  - ornith-ai/Ornith-1.5-35B-A3B-NVFP4
+  - Qwen/Qwen3-4B-Instruct-2507-FP8
+  - sahilchachra/Unlimited-OCR-AWQ
+  - byteshape/Qwen3.8-27B-GGUF
+  - gaunernst/gemma-3-27b-it-int4-awq
+  - Qwen/Qwen3-Coder-Next-FP8
+  - LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-AWQ
+  - Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice
+  - handy-computer/cohere-transcribe-03-2026-gguf
+  - KBLab/wav2vec2-large-voxrex-swedish
+  - RedHatAI/Qwen3.6-35B-A3B-NVFP4
+  - deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+  - unsloth/GLM-5.3-Flash-GGUF
+  - unsloth/Qwen3.6-35B-A3B-MTP-GGUF
+  - gabor-hosu/e5-mistral-7b-instruct-bnb-4bit
+  - nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8
+  - Qwen/Qwen3-32B-AWQ
+  - nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
+  - LiquidAI/LFM2.5-2.6B-GGUF
+  - nvidia/Qwen3.5-122B-A10B-NVFP4
+  - Qwen/Qwen2.5-Coder-32B-Instruct-AWQ
+  - RadixArk/Qwen3.8-27B-NVFP4
+  - cyankiwi/gemma-4-26B-A4B-it-AWQ-4bit
+  - google/gemma-2-9b-it
+  - FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree
+  - deepseek-ai/DeepSeek-V4-Flash
+  - openbmb/MiniCPM5-2B-DSpark
+  - Qwen/Qwen3-30B-A3B-Instruct-2507
+  - RedHatAI/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-FP8
+  - Inferact/Qwen3.8-27B-NVFP4
+  - thinkingmachines/Inkling
+  - kingabzpro/wav2vec2-large-xls-r-300m-Urdu
+  - MiniMaxAI/MiniMax-M2.7
+  - ornith-ai/Ornith-1.0-9B
+  - tencent/HunyuanOCR
+  - Qwen/Qwen-72B
+  - Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8
+  - unsloth/Qwen3.6-27B-NVFP4
+  - stabilityai/sdxl-turbo
+  - nvidia/Gemma-4-26B-A4B-NVFP4
+  - deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
+  - CMSManhattan/JiRackUltra_14b
+  - nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
+  - kresnik/wav2vec2-large-xlsr-korean
+  - unsloth/Z-Image-Turbo-GGUF
+  - unsloth/Qwen3.5-35B-A3B-GGUF
+  - Snowflake/snowflake-arctic-embed-l-v2.0
+  - RedHatAI/gemma-4-26B-A4B-it-FP8-dynamic
+  - Octen/Octen-Embedding-8B
+  - Lykon/dreamshaper-7
+  - cyankiwi/Qwen3.6-27B-AWQ-INT4
+  - unsloth/Inkling-Small-GGUF
+  - bartowski/endless-frontier_BigBang-v1-GGUF
+  - Qwen/Qwen2.5-Coder-32B-Instruct
+  - meta-llama/Meta-Llama-3-8B-Instruct
+  - cyankiwi/Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit
+  - google/medgemma-4b-it
+  - nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4
+  - google/gemma-4-12B-it-qat-w4a16-ct
+  - unsloth/Qwen3.6-35B-A3B-NVFP4
+  - Snowflake/snowflake-arctic-embed-l
+  - unsloth/Qwen-Image-2.1-GGUF
+  - Qwen/Qwen2.5-VL-32B-Instruct
+  - casperhansen/llama-3.3-70b-instruct-awq
+  - Qwen/Qwen2.5-7B
+  - theainerd/Wav2Vec2-large-xlsr-hindi
+  - google/gemma-4-12B-it-qat-q4_0-gguf
+  - Qwen/Qwen3-8B-Base
+  - comodoro/wav2vec2-xls-r-300m-cs-250
+  - jinaai/jina-embeddings-v5-omni-small
+  - Orion-zhen/Qwen2.5-Coder-7B-Instruct-AWQ
+  - meta-llama/Llama-2-7b-hf
+  - deepseek-ai/DeepSeek-OCR-2
+  - Infomaniak-AI/vllm-translategemma-4b-it
+  - deepseek-ai/DeepSeek-V4-Flash-Vision-Exp
+  - esatapedico/Qwen3.8-27B-NVFP4-MTP-GGUF
+  - ornith-ai/Ornith-1.5-9B-NVFP4
+  - Qwen/Qwen2.5-Coder-7B-Instruct-AWQ
+  - Yehor/w2v-xls-r-uk
+  - microsoft/Phi-3.5-vision-instruct
+  - AtomicChat/Qwen3.8-Flash-Next-GGUF
+  - Qwen/Qwen3-0.6B-Base
+  - meta-models/Muse-Glimmer-30B-GGUF
+  - google/gemma-4-E4B-it-qat-q4_0-gguf
+  - openbmb/MiniCPM-o-4_5
+  - microsoft/VibeVoice-ASR
+  - imvladikon/wav2vec2-xls-r-300m-hebrew
+  - datalab-to/surya-ocr-2-gguf
+  - unsloth/Qwen3.6-35B-A3B-NVFP4-Fast
+  - empero-ai/Qwen3.8-35B-A3B-Distill-GGUF
+  - ISTA-DASLab/gemma-3-27b-it-GPTQ-4b-128g
+  - meta-llama/Llama-3.2-1B
+  - microsoft/VibeVoice-1.5B
+  - QuantTrio/Qwen3.6-35B-A3B-AWQ
+  - black-forest-labs/FLUX.1-dev
+  - swiss-ai/Apertus-v1.5-8B
+  - ggml-org/gemma-4-E4B-it-GGUF
+  - Qwen/Qwen3-Omni-30B-A3B-Instruct
+  - Lightricks/LTX-Video
+  - nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
+  - NbAiLab/nb-wav2vec2-1b-bokmaal-v2
+  - nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4
+  - unsloth/Qwen3.6-27B-GGUF
+  - zai-org/GLM-5.2
+  - apple/OpenELM-1_1B-Instruct
+  - unsloth/Qwen3.6-27B-MTP-GGUF
+  - Qwen/Qwen2-VL-7B-Instruct
+  - deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
+  - thinkingmachines/Inkling-Small
+  - NbAiLab/nb-wav2vec2-1b-nynorsk
+  - black-forest-labs/FLUX.1-schnell
+  - Qwen/Qwen3.5-4B-Base
+  - Qwen/Qwen2.5-1.5B
+  - WhereIsAI/UAE-Large-V1
+  - meta-llama/Llama-3.1-8B
+  - OpenGVLab/InternVL2-1B
+  - OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF
+  - cl-nagoya/ruri-v3-310m
+  - deepseek-ai/deepseek-coder-7b-instruct-v1.5
+  - nvidia/Cosmos-Reason2-2B
+  - HuggingFaceTB/SmolLM3-3B
+  - yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF
+  - stabilityai/sd-turbo
+  - google/diffusiongemma-26B-A4B-it
+  - bartowski/Qwen2.5-32B-Instruct-GGUF
+  - empero-ai/Qwen3.8-9B-Distill-GGUF
+  - Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8
+  - nvidia/Qwen3.8-27B-NVFP4
+  - google/gemma-4-E4B
+  - microsoft/phi-2
+  - Tongyi-MAI/Z-Image-Turbo
+  - ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF
+  - handy-computer/parakeet-tdt-0.6b-v3-gguf
+  - Qwen/Qwen3-1.7B-Base
+  - nvidia/Qwen3.6-27B-NVFP4
+  - classla/wav2vec2-xls-r-parlaspeech-hr
+  - cyankiwi/MiniCPM-SALA-AWQ-8bit
+  - prism-ml/Ternary-Bonsai-27B-gguf
+  - Salesforce/blip2-opt-2.7b
+  - saattrupdan/wav2vec2-xls-r-300m-ftspeech
+  - Qwen/Qwen3-4B-Thinking-2507
+  - Qwen/Qwen3-4B-Base
+  - unsloth/gemma-4-E4B-it-GGUF
+  - nvidia/parakeet-tdt-0.6b-v3
+  - ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF
+  - Qwen/Qwen3-Coder-Next
+  - nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
+  - EleutherAI/pythia-6.9b
+  - sentence-transformers/all-roberta-large-v1
+  - Qwen/Qwen3-TTS-12Hz-0.6B-Base
+  - CompVis/stable-diffusion-v1-4
+  - handy-computer/whisper-medium-gguf
+  - empero-ai/Qwen3.8-4B-Distill-GGUF
+  - unsloth/Llama-3.2-1B-Instruct
+  - FINAL-Bench/POCKET-35B-GGUF
+  - Qwen/Qwen3-8B-FP8
+  - deepseek-ai/DeepSeek-V4-Flash-DSpark
+  - cyankiwi/Qwen3.8-27B-AWQ-INT4
+  - sentence-transformers/LaBSE
+  - Qwen/Qwen3.5-122B-A10B
+  - RedHatAI/Qwen3.8-27B-INT4
+  - distil-whisper/distil-large-v3
+  - openbmb/VoxCPM2
+  - unsloth/gemma-4-26B-A4B-it-GGUF
+  - OpenGVLab/InternVL2-2B
+  - ibm-granite/granite-4.1-3b
+  - QuantTrio/Qwen3.5-9B-AWQ
+  - CMSManhattan/JiRackDeltaNet_27b
+  - google/gemma-4-31B
+  - Qwen/Qwen2.5-72B-Instruct-AWQ
+  - Qwen/Qwen3-4B-GGUF
+  - Qwen/Qwen3-8B-GGUF
+  - Qwen/Qwen2-1.5B-Instruct
+  - QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ
+  - stepfun-ai/GOT-OCR2_0
+  - SG161222/RealVisXL_V5.0
+  - ai-sage/Giga-Embeddings-instruct
+  - MiniMaxAI/MiniMax-M2.5
+  - abenzerps/Apodex-1.1-mini-GGUF
+  - google/gemma-2-2b-it
+  - empero-ai/Qwen3.8-2B-Distill-GGUF
+  - SG161222/Realistic_Vision_V5.1_noVAE
+  - Qwen/Qwen2.5-Coder-3B-Instruct
+  - NousResearch/Hermes-3-Llama-3.1-8B
+  - setu4993/LaBSE
+  - RedHatAI/Llama-3.2-3B-Instruct-FP8-dynamic
+  - empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF
+  - boboliu/bge-reranker-v2.5-gemma2-lightweight-gptq
+  - Qwen/Qwen3.5-122B-A10B-FP8
+  - Qwen/Qwen3-235B-A22B
+  - ornith-ai/Ornith-1.5-35B-A3B-FP8
+  - Alibaba-NLP/gte-Qwen2-1.5B-instruct
+  - zai-org/GLM-5
+  - zai-org/GLM-4.5-Air
+  - Qwen/Qwen2.5-Coder-1.5B-Instruct
+  - LeaderboardModel1/zeta-2.1-autoround-W4A16
+  - unsloth/gemma-4-12B-it-qat-GGUF
+  - bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF
+  - XHToken/Spark-X2.5-4B-GGUF
+  - llava-hf/llava-onevision-qwen2-0.5b-ov-hf
+  - bartowski/Qwen3.8-27B-GGUF
+  - Qwen/Qwen3-ASR-0.6B
+  - unsloth/gpt-oss-20b-GGUF
+  - allenai/Olmo-3-7B-Think
+  - bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
+  - unsloth/GLM-5.3-GGUF
+  - Qwen/Qwen2.5-32B-Instruct-GPTQ-Int4
+  - nlpai-lab/KURE-v1
+  - unsloth/Qwen-Image-Edit-2511-GGUF
+  - Qwen/Qwen3.5-0.8B-Base
+  - stelterlab/Qwen3-30B-A3B-Instruct-2507-AWQ
+  - meta-llama/Llama-3.1-405B-FP8
+  - openbmb/MiniCPM5-2B-MLX
+  - Qwen/Qwen2.5-Coder-7B-Instruct-GPTQ-Int4
+  - allenai/Olmo-3-7B-Instruct
+  - nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16
+  - nvidia/Qwen3-14B-NVFP4
+  - Qwen/Qwen-Image-Edit-2509
+  - ByteDance-Seed/UI-TARS-1.5-7B
+  - unsloth/Qwen3-4B-GGUF
+  - nomic-ai/nomic-embed-code
+  - bigscience/bloom-560m
+  - ornith-ai/Ornith-1.5-9B
+  - unsloth/gemma-4-26B-A4B-it-qat-GGUF
+  - Qwen/Qwen3.5-397B-A17B
+  - swiss-ai/Apertus-8B-Instruct-2509
+  - nvidia/GLM-5.2-NVFP4
+  - moonshotai/Kimi-K2.6
+  - k-chirkunov/gemma4-e4b-claims-comparison
+  - gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090
+  - Qwen/Qwen2.5-Coder-7B
+  - google/gemma-3-12b-it
+  - thenlper/gte-large
+  - Qwen/Qwen2.5-3B-Instruct-AWQ
+  - google/gemma-4-E4B-it-qat-w4a16-ct
+  - unsloth/inkling-GGUF
+  - mistralai/Mistral-7B-v0.1
+  - RedHatAI/gemma-4-31B-it-FP8-block
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+  - Maincode/matilda-jev-v1
+  - FINAL-Bench/Darwin-180B-RSI-R3
+  - vcruz305/RED-SNOW-5.3-FLASH-EXL3-SAGE-2.49bpw
+  - h2oai/h2o-lightning-4b
+  - Blackfrost-AI/RED-SNOW-5.3-FLASH-BF16
+  - kandinskylab/Kandinsky-6.0-Lite-distill-5s-Diffusers
+  - kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers
+  - Ateron/Gemma-4-Writers-31B-V2
+  - audreyt/Kolibri-1-NVFP4-W4A16
+  - lighteternal/biodecision-v2-4b
+  - malteos/most-embed-de
+  - kandinskylab/Kandinsky-6.0-Lite-5s-Diffusers
+  - ConwayResearch/Underdog-Saluki-27B-1.0
+  - PleIAs/Baguettotron-MoE
+  - JetBrains/Mellum2.1-12B-A2.5B-Thinking
+  - kandinskylab/Kandinsky-6.0-Pro-pretrain-5s-Diffusers
+  - kandinskylab/Kandinsky-6.0-Lite-pretrain-5s-Diffusers
+  - aina-tech/Anima-Lightning
+  - amazon/ALoDLM-1.7B
+  - facebook/meta-encoder
+  - speridlabs/iris-3b
+  - Modularcomputing/Native-Bird
+  - lightonai/LightOnOCR-3-0.8B
+  - lightonai/LightOnOCR-3-4B
+  - ApolloRaines/LTX-2.5-22b-OmniGen-v12
+  - JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF
+  - Kujira/Underdog-Saluki-27B-1.0-MTP-GGUF
+  - DavidAU/Qwen3.8-27B-Turbo-Brilliance-Power-35X-Reasoning-Instruct-modes-GGUF
+  - inclusionAI/Ming-Image-0.1-Design
+  - Aratako/Irodori-TTS-v4-Large
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# VRAM & GPU Cost Calculator
+
+Pick any Hugging Face model and see:
+
+- how much GPU memory it needs to run, fine-tune with LoRA, fully fine-tune, generate, transcribe or embed, at its published precision or at 16, 8 or 4 bits;
+- the cheapest GPU setups that hold it (one H100, two A100s, four RTX 4090s...), each priced live across the GPU clouds [FastGPU](https://fastgpu.co/?utm_source=huggingface&utm_medium=space&utm_campaign=gpu-cost-calculator) tracks, with a link to every offer.
+
+It reads each model's own metadata from the Hub (the parameter count in its safetensors or GGUF header, its quantization config) and asks FastGPU's free match API for the sizing and the live prices. Nothing to install, no sign-in. Type a size such as `70B` for a model that is not on the Hub.
+
+## How the memory is sized
+
+The same way as on fastgpu.co, by FastGPU's matcher:
+
+- **Run / serve:** the weights (parameters × bytes per parameter: 2 at 16-bit, 1 at 8-bit, 0.5 at 4-bit), plus the KV cache for one request at a 4K-token context and runtime headroom.
+- **Fine-tune with LoRA:** the frozen weights plus the adapters, their optimizer state and activations.
+- **Full fine-tune:** about 16 bytes per parameter (weights, gradients and Adam states) plus activations.
+- **Image, video, speech and embedding models** FastGPU knows (FLUX, SDXL, Wan, Whisper and more) use a figure for the whole pipeline, text encoders and VAE included.
+
+A quantized repo (AWQ, GPTQ, FP8, NVFP4, bitsandbytes, GGUF) is sized at the bits it is stored in unless you pick another precision. The full method: [fastgpu.co/methodology](https://fastgpu.co/methodology?utm_source=huggingface&utm_medium=space&utm_campaign=gpu-cost-calculator).
+
+## Where the prices come from
+
+FastGPU collects the GPU rental prices providers publish (marketplaces, neoclouds and hyperscalers) and refreshes them through the day. Each price here is the provider's own per-GPU rate times the number of GPUs in the setup. "Lowest price" (the default) sorts by price alone. "Best match" orders by FastGPU's score, led by price and weighing reliability and availability; providers that pay FastGPU a referral fee are marked ★ and can only win a near-tie there, within a few percent of the cheapest.
+
+The price history is open: [fastgpu/cloud-gpu-prices](https://huggingface.co/datasets/fastgpu/cloud-gpu-prices) (CC BY 4.0, updated daily).
+
+## Use it from code
+
+```bash
+curl "https://fastgpu.co/api/v1/match?model=Qwen/Qwen3-32B&params_b=32.8&task=inference"
+```
+
+No key needed. Docs: [fastgpu.co/docs](https://fastgpu.co/docs?utm_source=huggingface&utm_medium=space&utm_campaign=gpu-cost-calculator).

app.js

@@ -0,0 +1,535 @@
+/*
+ * VRAM & GPU cost calculator (FastGPU's Hugging Face Space).
+ *
+ * Runs entirely in the visitor's browser. Two public, keyless reads:
+ *   1. the Hugging Face Hub API, for the model's own metadata (parameter count from its safetensors or GGUF
+ *      header, its weight format, its task);
+ *   2. FastGPU's match API (https://fastgpu.co/api/v1/match), which sizes the job and ranks the GPU setups
+ *      that hold it at live prices. All sizing is FastGPU's, so this page and fastgpu.co never disagree.
+ * The model list in catalog.js only powers search and quick picks; every selection is re-read live.
+ */
+(function () {
+  "use strict";
+
+  const SITE = "https://fastgpu.co";
+  const API = SITE + "/api/v1/match";
+  const GPUS_API = SITE + "/api/v1/gpus";
+  const HUB = "https://huggingface.co";
+  const UTM = { utm_source: "huggingface", utm_medium: "space", utm_campaign: "gpu-cost-calculator" };
+  const DEFAULT_MODEL = "Qwen/Qwen3-32B";
+  const PICKS = window.FASTGPU_PICKS || [];
+  const CATALOG = (window.FASTGPU_CATALOG || []).map(function (r) {
+    return { id: r[0], p: r[1], task: r[2], dl: r[3], q: r[4], low: r[0].toLowerCase() };
+  });
+  const BY_ID = new Map(CATALOG.map(function (m) { return [m.low, m]; }));
+
+  const TASKS = {
+    inference: "Run / serve it",
+    "finetune-lora": "Fine-tune with LoRA",
+    "finetune-full": "Full fine-tune",
+    generate: "Generate images or video",
+    transcribe: "Transcribe speech",
+    embed: "Embed text",
+  };
+  const PIPELINE_TASK = {
+    "text-generation": "inference", "image-text-to-text": "inference", "any-to-any": "inference",
+    "text2text-generation": "inference", "visual-question-answering": "inference", "text-to-speech": "inference",
+    "text-to-image": "generate", "image-to-image": "generate", "text-to-video": "generate", "image-to-video": "generate",
+    "automatic-speech-recognition": "transcribe",
+    "feature-extraction": "embed", "sentence-similarity": "embed", "text-ranking": "embed",
+  };
+  const PRECISION_TEXT = { fp16: "16-bit", int8: "8-bit", int4: "4-bit" };
+  const BYTES = { fp16: 2, int8: 1, int4: 0.5 };
+
+  const $ = function (id) { return document.getElementById(id); };
+  const el = {
+    form: $("calc"), input: $("model"), list: $("suggestions"), picks: $("picks"),
+    task: $("task"), prec: $("precision"), region: $("region"), spot: $("spot"),
+    out: $("result"), status: $("status"),
+  };
+
+  const state = {
+    sel: null,        // { id, paramsB, paramsSource, bits, format, pipeline, gated } or { size: N }
+    result: null,     // last match API answer
+    sort: "price",
+    gpuMemory: null,  // { "H100 SXM": 80, ... } from FastGPU's GPU list
+    seq: 0,           // request counter, so a slow answer never overwrites a newer one
+    active: -1,       // highlighted suggestion
+    options: [],
+  };
+
+  // ---------------------------------------------------------------- helpers
+  function link(url) {
+    const u = new URL(url, SITE);
+    if (u.hostname.endsWith("fastgpu.co")) Object.keys(UTM).forEach(function (k) { u.searchParams.set(k, UTM[k]); });
+    return u.toString();
+  }
+  function esc(s) {
+    return String(s).replace(/[&<>"']/g, function (c) { return { "&": "&amp;", "<": "&lt;", ">": "&gt;", '"': "&quot;", "'": "&#39;" }[c]; });
+  }
+  function usd(n) {
+    if (n == null || !isFinite(n)) return "n/a";
+    const digits = n < 100 ? 2 : 0;
+    return "$" + n.toLocaleString("en-US", { minimumFractionDigits: digits, maximumFractionDigits: digits });
+  }
+  function gb(n) {
+    if (n >= 100) return Math.round(n).toLocaleString("en-US");
+    if (n >= 1) return n.toFixed(1).replace(/\.0$/, "");
+    return n.toFixed(2).replace(/0$/, "");
+  }
+  // The parameter count as shown (32.76 -> 32.8), so every figure derived on screen uses the number on screen.
+  function shownParams(p) {
+    if (p >= 1000) return Math.round(p / 10) * 10;
+    if (p >= 10) return Math.round(p * 10) / 10;
+    return Math.round(p * 100) / 100;
+  }
+  function sizeText(p) {
+    const v = shownParams(p);
+    return v >= 1000 ? (v / 1000).toString() + "T" : v.toString() + "B";
+  }
+  function ago(iso) {
+    const t = Date.parse(iso);
+    if (!isFinite(t)) return null;
+    const min = Math.max(0, Math.round((Date.now() - t) / 60000));
+    if (min < 1) return "just now";
+    if (min < 60) return min + " min ago";
+    const h = Math.round(min / 60);
+    return h + (h === 1 ? " hour ago" : " hours ago");
+  }
+  function timeout(ms) {
+    const c = new AbortController();
+    setTimeout(function () { c.abort(); }, ms);
+    return c.signal;
+  }
+  async function getJson(url, ms) {
+    const r = await fetch(url, { signal: timeout(ms) });
+    if (!r.ok) {
+      const e = new Error("HTTP " + r.status);
+      e.status = r.status;
+      throw e;
+    }
+    return r.json();
+  }
+
+  // The total size a repo name states ("Qwen3-30B-A3B" -> 30), skipping the active ("A3B") and expert ("8x7B") forms.
+  function nameSizeB(id) {
+    const name = (id.split("/").pop() || "").replace(/_/g, "-");
+    const m = name.match(/(?<![\w.])(\d+(?:\.\d+)?)[Bb](?![a-zA-Z0-9])/);
+    return m ? parseFloat(m[1]) : null;
+  }
+
+  // Bits the published weights are stored at (4 or 8), from the repo's quantization config, its tensors, or its name.
+  function weightBits(meta, id) {
+    const qc = (meta.config && meta.config.quantization_config) || {};
+    const method = String(qc.quant_method || "").toLowerCase();
+    if (["awq", "gptq", "hqq", "aqlm", "quark"].indexOf(method) >= 0) return (qc.bits || qc.w_bit || qc.nbits) === 8 ? 8 : 4;
+    if (method === "bitsandbytes") return qc.load_in_4bit ? 4 : qc.load_in_8bit ? 8 : null;
+    if (method === "mxfp4" || method === "nvfp4") return 4;
+    if (["fp8", "fbgemm_fp8", "eetq"].indexOf(method) >= 0) return 8;
+    if (method === "compressed-tensors") {
+      const bits = Object.values(qc.config_groups || {}).map(function (g) { return g && g.weights && g.weights.num_bits; }).filter(Boolean);
+      if (bits.length) { const b = Math.min.apply(null, bits); if (b === 4 || b === 8) return b; }
+    }
+    const top = dominantDtype(meta);
+    if (top && top.indexOf("F8") === 0) return 8;
+    if (meta.gguf && !meta.safetensors) return 4;
+    const n = id.toLowerCase();
+    if (/(^|[-_./])(4bit|4-bit|int4|w4a16|awq|gptq|nf4|mxfp4|nvfp4|q4)/.test(n)) return 4;
+    if (/(^|[-_./])(8bit|8-bit|int8|w8a8|w8a16|fp8)/.test(n)) return 8;
+    return null;
+  }
+  function dominantDtype(meta) {
+    const p = meta.safetensors && meta.safetensors.parameters;
+    if (!p) return null;
+    let best = null;
+    Object.keys(p).forEach(function (k) { if (!best || p[k] > p[best]) best = k; });
+    return best;
+  }
+  function formatName(meta, bits) {
+    const qc = (meta.config && meta.config.quantization_config) || {};
+    const method = String(qc.quant_method || "").toUpperCase();
+    if (meta.gguf && !meta.safetensors) return "GGUF";
+    if (method && method !== "COMPRESSED-TENSORS" && method !== "BITSANDBYTES") return method + (bits ? " " + bits + "-bit" : "");
+    if (bits) return bits + "-bit";
+    const top = dominantDtype(meta);
+    return top || null;
+  }
+
+  // ---------------------------------------------------------------- model metadata
+  async function readModel(id) {
+    const path = id.split("/").map(encodeURIComponent).join("/");
+    const q = ["safetensors", "gguf", "config", "pipeline_tag", "library_name", "gated", "downloads", "likes"]
+      .map(function (k) { return "expand[]=" + k; }).join("&");
+    const meta = await getJson(HUB + "/api/models/" + path + "?" + q, 8000);
+    const total = ((meta.safetensors && meta.safetensors.total) || (meta.gguf && meta.gguf.total) || 0) / 1e9;
+    const stated = nameSizeB(meta.id || id);
+    let paramsB = null, source = null;
+    if (total && stated && total < 0.6 * stated) { paramsB = stated; source = "name"; }
+    else if (total) { paramsB = total; source = meta.safetensors && meta.safetensors.total ? "safetensors" : "gguf"; }
+    else if (stated) { paramsB = stated; source = "name"; }
+    const bits = weightBits(meta, meta.id || id);
+    return {
+      id: meta.id || id,
+      paramsB: paramsB,
+      paramsSource: source,
+      bits: bits,
+      format: formatName(meta, bits),
+      pipeline: meta.pipeline_tag || null,
+      library: meta.library_name || null,
+      gated: !!meta.gated,
+    };
+  }
+
+  // ---------------------------------------------------------------- search box
+  function sizeQuery(text) {
+    const m = text.trim().match(/^~?(\d+(?:\.\d+)?)\s*([bt])?(?:\s*(?:params?|parameters?))?$/i);
+    if (!m) return null;
+    const n = parseFloat(m[1]) * (m[2] && m[2].toLowerCase() === "t" ? 1000 : 1);
+    return n >= 0.05 && n <= 5000 ? n : null;
+  }
+  function suggest(text) {
+    const t = text.trim().toLowerCase();
+    const out = [];
+    if (!t) {
+      PICKS.forEach(function (id) { const m = BY_ID.get(id.toLowerCase()); if (m) out.push({ kind: "model", m: m }); });
+      return out.slice(0, 8);
+    }
+    const size = sizeQuery(t);
+    if (size) out.push({ kind: "size", size: size });
+    const words = t.split(/[\s/]+/).filter(Boolean);
+    const scored = [];
+    for (let i = 0; i < CATALOG.length; i++) {
+      const m = CATALOG[i];
+      if (!words.every(function (w) { return m.low.indexOf(w) >= 0; })) continue;
+      const name = m.low.split("/").pop();
+      const score = (m.low === t ? 4 : 0) + (name.indexOf(t) === 0 ? 2 : 0) + (m.low.indexOf(t) >= 0 ? 1 : 0);
+      scored.push({ m: m, score: score });
+    }
+    scored.sort(function (a, b) { return b.score - a.score || b.m.dl - a.m.dl; });
+    scored.slice(0, 8).forEach(function (s) { out.push({ kind: "model", m: s.m }); });
+    if (/^[\w.-]+\/[\w.-]+$/.test(text.trim()) && !BY_ID.has(t)) out.unshift({ kind: "hub", id: text.trim() });
+    return out.slice(0, 9);
+  }
+  function renderSuggestions() {
+    const opts = state.options;
+    if (!opts.length) { closeSuggestions(); return; }
+    el.list.innerHTML = opts.map(function (o, i) {
+      const sel = i === state.active ? ' aria-selected="true"' : "";
+      if (o.kind === "size") return '<li role="option" id="opt-' + i + '"' + sel + ' data-i="' + i + '"><span class="opt-name">A ' + esc(sizeText(o.size)) + "-parameter model</span><span class=\"opt-meta\">size only</span></li>";
+      if (o.kind === "hub") return '<li role="option" id="opt-' + i + '"' + sel + ' data-i="' + i + '"><span class="opt-name">' + esc(o.id) + '</span><span class="opt-meta">look it up on Hugging Face</span></li>';
+      return '<li role="option" id="opt-' + i + '"' + sel + ' data-i="' + i + '"><span class="opt-name">' + esc(o.m.id) + '</span><span class="opt-meta">' + esc(sizeText(o.m.p)) + " · " + esc(TASKS[o.m.task] || o.m.task) + "</span></li>";
+    }).join("");
+    el.list.hidden = false;
+    el.input.setAttribute("aria-expanded", "true");
+    el.input.setAttribute("aria-activedescendant", state.active >= 0 ? "opt-" + state.active : "");
+  }
+  function closeSuggestions() {
+    el.list.hidden = true;
+    el.input.setAttribute("aria-expanded", "false");
+    el.input.removeAttribute("aria-activedescendant");
+    state.active = -1;
+  }
+  function choose(o) {
+    closeSuggestions();
+    if (!o) return;
+    if (o.kind === "size") { el.input.value = sizeText(o.size) + " model"; selectSize(o.size); return; }
+    const id = o.kind === "hub" ? o.id : o.m.id;
+    el.input.value = id;
+    selectModel(id, true);
+  }
+
+  el.input.addEventListener("input", function () {
+    state.options = suggest(el.input.value);
+    state.active = state.options.length ? 0 : -1;
+    renderSuggestions();
+  });
+  el.input.addEventListener("focus", function () {
+    state.options = suggest(el.input.value === (state.sel && state.sel.id) ? "" : el.input.value);
+    state.active = -1;
+    renderSuggestions();
+  });
+  el.input.addEventListener("keydown", function (e) {
+    if (e.key === "ArrowDown" || e.key === "ArrowUp") {
+      if (el.list.hidden) { state.options = suggest(el.input.value); }
+      const n = state.options.length;
+      if (!n) return;
+      e.preventDefault();
+      state.active = e.key === "ArrowDown" ? (state.active + 1) % n : (state.active - 1 + n) % n;
+      renderSuggestions();
+    } else if (e.key === "Enter") {
+      e.preventDefault();
+      if (!el.list.hidden && state.active >= 0) { choose(state.options[state.active]); return; }
+      const opts = suggest(el.input.value);
+      choose(opts[0]);
+    } else if (e.key === "Escape") {
+      closeSuggestions();
+    }
+  });
+  el.list.addEventListener("mousedown", function (e) {
+    const li = e.target.closest("li[data-i]");
+    if (!li) return;
+    e.preventDefault();
+    choose(state.options[Number(li.getAttribute("data-i"))]);
+  });
+  el.input.addEventListener("blur", function () { setTimeout(closeSuggestions, 120); });
+
+  el.picks.innerHTML = PICKS.filter(function (id) { return BY_ID.has(id.toLowerCase()); }).map(function (id) {
+    return '<button type="button" class="chip" data-id="' + esc(id) + '">' + esc(id.split("/").pop()) + "</button>";
+  }).join("");
+  el.picks.addEventListener("click", function (e) {
+    const b = e.target.closest("button[data-id]");
+    if (!b) return;
+    el.input.value = b.getAttribute("data-id");
+    selectModel(b.getAttribute("data-id"), true);
+  });
+
+  [el.task, el.prec, el.region, el.spot].forEach(function (c) {
+    c.addEventListener("change", function () { saveHash(); run(); });
+  });
+  el.form.addEventListener("submit", function (e) { e.preventDefault(); });
+
+  // ---------------------------------------------------------------- selection
+  async function selectModel(id, resetTask) {
+    const seq = ++state.seq;
+    const known = BY_ID.get(id.toLowerCase());
+    setStatus("Reading " + id + " from Hugging Face…");
+    let sel;
+    try {
+      sel = await readModel(id);
+    } catch (err) {
+      if (seq !== state.seq) return;
+      if (known) {
+        sel = { id: known.id, paramsB: known.p, paramsSource: "catalog", bits: known.q, format: known.q ? known.q + "-bit" : null, pipeline: null, gated: false };
+      } else {
+        showError(err && err.status === 404
+          ? "Hugging Face has no public model called " + esc(id) + ". Check the spelling, or type a size such as 70B."
+          : "Could not read " + esc(id) + " from Hugging Face just now. Try again, or type a size such as 70B.");
+        return;
+      }
+    }
+    if (seq !== state.seq) return;
+    state.sel = sel;
+    if (resetTask) {
+      const t = (sel.pipeline && PIPELINE_TASK[sel.pipeline]) || (known && known.task) || "inference";
+      el.task.value = t;
+      el.prec.value = "auto";
+    }
+    saveHash();
+    run();
+  }
+  function selectSize(size) {
+    state.sel = { size: size };
+    el.prec.value = el.prec.value || "auto";
+    saveHash();
+    run();
+  }
+
+  function autoPrecision() {
+    const s = state.sel;
+    if (!s || !s.bits) return undefined;
+    return s.bits === 4 ? "int4" : "int8";
+  }
+
+  // ---------------------------------------------------------------- match
+  async function run() {
+    const s = state.sel;
+    if (!s) return;
+    const seq = ++state.seq;
+    const params = new URLSearchParams();
+    if (s.id) params.set("model", s.id);
+    const paramsB = s.id ? s.paramsB : s.size;
+    if (paramsB) params.set("params_b", String(Math.round(paramsB * 100) / 100));
+    params.set("task", el.task.value);
+    const prec = el.prec.value === "auto" ? autoPrecision() : el.prec.value;
+    if (prec) params.set("precision", prec);
+    if (el.region.value) params.set("region", el.region.value);
+    if (el.spot.checked) params.set("spot", "true");
+    if (s.id && !paramsB) {
+      showError("The repo " + esc(s.id) + " does not publish a parameter count. Type its size instead, for example 70B.");
+      return;
+    }
+    setStatus("Pricing the GPUs that fit…");
+    let res = null;
+    try {
+      const both = await Promise.all([
+        fetch(API + "?" + params.toString(), { signal: timeout(15000) }).then(function (r) {
+          return r.json().catch(function () { return null; }).then(function (body) { return { ok: r.ok, body: body }; });
+        }),
+        loadGpuMemory(),
+      ]);
+      res = both[0];
+    } catch (err) {
+      res = null;
+    }
+    if (seq !== state.seq) return;
+    if (res && res.body && (res.ok || res.body.error)) {
+      state.result = res.body;
+      render();
+      return;
+    }
+    showError("FastGPU's price feed did not answer in time. <button type=\"button\" class=\"linklike\" id=\"retry\">Try again</button>");
+    const b = $("retry");
+    if (b) b.addEventListener("click", run);
+  }
+  async function loadGpuMemory() {
+    if (state.gpuMemory) return;
+    try {
+      const d = await getJson(GPUS_API, 10000);
+      const map = {};
+      (d.gpus || []).forEach(function (g) { if (g.gpu && g.vram_gb) map[g.gpu] = g.vram_gb; });
+      state.gpuMemory = map;
+    } catch (e) {
+      state.gpuMemory = {};
+    }
+  }
+
+  // ---------------------------------------------------------------- render
+  function setStatus(text) {
+    el.status.textContent = text;
+    el.out.setAttribute("aria-busy", "true");
+    el.out.classList.add("loading");
+  }
+  function showError(html) {
+    el.out.classList.remove("loading");
+    el.out.removeAttribute("aria-busy");
+    el.status.textContent = "";
+    el.out.innerHTML = '<div class="notice">' + html + "</div>";
+  }
+
+  function needSentence(task, precText) {
+    switch (task) {
+      case "finetune-lora": return "of GPU memory to fine-tune it with LoRA at " + precText;
+      case "finetune-full": return "of GPU memory for a full fine-tune";
+      case "generate": return "of GPU memory to generate with it at " + precText;
+      case "transcribe": return "of GPU memory to transcribe with it at " + precText;
+      case "embed": return "of GPU memory to embed with it at " + precText;
+      default: return "of GPU memory to run it at " + precText;
+    }
+  }
+  function derivation(task, rawParamsB, precision, need) {
+    const bytes = BYTES[precision];
+    const paramsB = shownParams(rawParamsB);
+    const weights = paramsB * bytes;
+    const w = "Weights: " + sizeText(paramsB) + " parameters × " + bytes + (bytes === 1 ? " byte" : " bytes") + " = " + gb(weights) + " GB.";
+    const rest = need - weights;
+    if (task === "finetune-full") {
+      return "Full fine-tuning holds the weights, their gradients and Adam optimizer states, about 16 bytes per parameter (" + sizeText(paramsB) + " × 16 = " + gb(paramsB * 16) + " GB), plus activations.";
+    }
+    if (rest < 0.5) return w;
+    if (task === "inference") return w + " The other ~" + gb(rest) + " GB is the KV cache for one request at a 4K-token context, plus runtime headroom.";
+    if (task === "finetune-lora") return w + " The other ~" + gb(rest) + " GB is the LoRA adapters, their optimizer state and activations for a 4K-token batch.";
+    if (task === "generate") return w + " The other ~" + gb(rest) + " GB covers the text encoders, VAE and working memory an image or video pipeline loads with it.";
+    return w + " The other ~" + gb(rest) + " GB is activations and runtime headroom.";
+  }
+
+  function render() {
+    const res = state.result;
+    const s = state.sel;
+    el.out.classList.remove("loading");
+    el.out.removeAttribute("aria-busy");
+    el.status.textContent = "";
+    if (!res || res.error) { showError(esc((res && res.error) || "No answer from FastGPU.")); return; }
+    const w = res.workload || {};
+    // FastGPU decides the job's kind for a model it knows (an image model is sized as a pipeline), so the
+    // sentences follow its answer rather than the menu.
+    const task = TASKS[w.task] ? w.task : el.task.value;
+    const precision = w.precision || "fp16";
+    const precText = PRECISION_TEXT[precision] || precision;
+    const paramsB = s.id ? s.paramsB : s.size;
+    const need = w.vram_required_gb;
+
+    let head = "";
+    if (s.id) {
+      const facts = [sizeText(paramsB) + " parameters"];
+      if (s.format) facts.push(esc(s.format) + " weights");
+      if (s.pipeline) facts.push(esc(s.pipeline));
+      const src = { safetensors: "the repo's safetensors metadata", gguf: "the repo's GGUF header", name: "the model's name (the Hub's count of its packed weights is lower)", catalog: "this Space's model list (Hugging Face did not answer just now)" }[s.paramsSource] || "the repo";
+      head = '<div class="model"><a class="model-id" href="' + HUB + "/" + esc(s.id) + '" target="_blank" rel="noopener">' + esc(s.id) + "</a>" +
+        '<p class="facts">' + facts.join(" · ") + "</p>" +
+        '<p class="fineprint">Parameter count from ' + src + "." + (s.library === "mlx" ? " MLX builds run on Apple silicon; sized here as the same model on a rented GPU." : "") + "</p></div>";
+    } else {
+      head = '<div class="model"><span class="model-id">A ' + esc(sizeText(paramsB)) + "-parameter model</span></div>";
+    }
+
+    const needHtml = need
+      ? '<div class="need"><p class="need-num"><span class="mono">~' + esc(Math.round(need).toLocaleString("en-US")) + "</span> GB</p>" +
+        '<p class="need-what">' + esc(needSentence(task, precText)) + "</p>" +
+        '<p class="fineprint">' + esc(derivation(task, paramsB, precision, need)) +
+        ' <a href="' + link(SITE + "/methodology") + '" target="_blank" rel="noopener">How FastGPU sizes and ranks</a></p></div>'
+      : "";
+
+    let rows = (res.matches || []).slice();
+    if (state.sort === "price") rows.sort(function (a, b) { return a.effective_usd_hr - b.effective_usd_hr; });
+    const cheapest = rows.reduce(function (min, r) { return Math.min(min, r.effective_usd_hr); }, Infinity);
+    const mem = state.gpuMemory || {};
+
+    let table;
+    if (!rows.length) {
+      table = '<div class="notice">No GPU setup in FastGPU\'s live feed holds this ' + (need ? "(~" + esc(Math.round(need)) + " GB) " : "") + "right now with these filters. Try 8-bit or 4-bit weights, another region, or include spot capacity.</div>";
+    } else {
+      table = '<div class="table-head"><h2 id="setups">GPU setups that hold it, priced live</h2>' +
+        '<div class="sort" role="group" aria-label="Order">' +
+        '<button type="button" data-sort="price" aria-pressed="' + (state.sort === "price") + '">Lowest price</button>' +
+        '<button type="button" data-sort="match" aria-pressed="' + (state.sort === "match") + '">Best match</button></div></div>' +
+        '<table aria-labelledby="setups"><thead><tr><th scope="col">Setup</th><th scope="col" class="num">Per hour</th><th scope="col" class="num">Per month</th><th scope="col">Provider</th></tr></thead><tbody>' +
+        rows.map(function (r) {
+          const card = mem[r.gpu];
+          const setup = (r.gpu_count > 1 ? r.gpu_count + "× " : "1× ") + r.gpu;
+          const sub = (card ? card + " GB " + (r.gpu_count > 1 ? "each, " : "") : "") + (r.fits_single_card ? "fits on one card" : "split across " + r.gpu_count + " cards");
+          const kind = [r.offer_type, r.reliability].filter(function (x, i, a) {
+            return x && a.findIndex(function (y) { return y && y.toLowerCase() === x.toLowerCase(); }) === i;
+          }).join(" · ");
+          return "<tr" + (r.effective_usd_hr === cheapest ? ' class="cheapest"' : "") + ">" +
+            '<th scope="row"><span class="setup">' + esc(setup) + '</span><span class="sub">' + esc(sub) + "</span></th>" +
+            '<td class="num"><a class="price" href="' + link(r.page_url) + '" target="_blank" rel="noopener" title="Every live offer for the ' + esc(r.gpu) + ' on FastGPU">' + esc(usd(r.effective_usd_hr)) + "</a>" +
+            (r.effective_usd_hr === cheapest ? '<span class="sub">cheapest</span>' : "") + "</td>" +
+            '<td class="num mono">' + esc(usd(r.monthly_usd)) + "</td>" +
+            "<td>" + esc(r.provider_label) + (r.partner ? ' <span class="star" title="Pays FastGPU a referral fee">★</span>' : "") + '<span class="sub">' + esc(kind) + "</span></td></tr>";
+        }).join("") + "</tbody></table>";
+    }
+
+    const fresh = res.updated_at ? ago(res.updated_at) : null;
+    const foot = '<p class="fineprint">' +
+      (fresh ? "Prices from FastGPU's live feed, refreshed " + esc(fresh) + ". " : "") +
+      "Each price is the whole setup per hour (per-GPU rate × GPUs), per month at 730 hours; it links to every live offer for that GPU. " +
+      "Best match orders by FastGPU's score, led by price and weighing reliability and availability; providers marked ★ pay FastGPU a referral fee and can only win a near-tie there, within a few percent of the cheapest. " +
+      "Some providers bill CPU, RAM or disk on top of the GPU rate; the GPU page says which.</p>" +
+      '<p class="cta"><a class="button" href="' + link(SITE + "/") + '" target="_blank" rel="noopener">Compare every GPU on FastGPU</a>' +
+      '<a href="' + link(SITE + "/gpus") + '" target="_blank" rel="noopener">All live GPU prices</a></p>';
+
+    el.out.innerHTML = head + needHtml + table + foot;
+    el.out.querySelectorAll("button[data-sort]").forEach(function (b) {
+      b.addEventListener("click", function () { state.sort = b.getAttribute("data-sort"); render(); });
+    });
+  }
+
+  // ---------------------------------------------------------------- shareable state
+  function saveHash() {
+    const s = state.sel;
+    if (!s) return;
+    const h = new URLSearchParams();
+    if (s.id) h.set("model", s.id); else h.set("size", String(s.size));
+    if (el.task.value !== "inference") h.set("task", el.task.value);
+    if (el.prec.value !== "auto") h.set("precision", el.prec.value);
+    if (el.region.value) h.set("region", el.region.value);
+    if (el.spot.checked) h.set("spot", "1");
+    try { history.replaceState(null, "", "#" + h.toString()); } catch (e) { /* sandboxed frame */ }
+  }
+  function start() {
+    const raw = (location.hash || "").replace(/^#/, "") || (location.search || "").replace(/^\?/, "");
+    const h = new URLSearchParams(raw);
+    const task = h.get("task");
+    if (task && TASKS[task]) el.task.value = task;
+    const prec = h.get("precision");
+    if (prec && ["fp16", "int8", "int4"].indexOf(prec) >= 0) el.prec.value = prec;
+    const region = h.get("region");
+    if (region && ["US", "EU", "ASIA"].indexOf(region) >= 0) el.region.value = region;
+    el.spot.checked = h.get("spot") === "1";
+    const size = h.get("size") ? parseFloat(h.get("size")) : NaN;
+    if (isFinite(size) && size > 0) { el.input.value = sizeText(size) + " model"; selectSize(size); return; }
+    const model = h.get("model") || DEFAULT_MODEL;
+    el.input.value = model;
+    selectModel(model, !task);
+  }
+  start();
+})();

catalog.js

@@ -0,0 +1,4 @@
+/* Models the calculator offers in search: [repo id, parameters (billions), job, monthly downloads, weight bits].
+   Generated by scripts/build_space.py from the Hugging Face Hub API; each selection is re-read live. */
+window.FASTGPU_PICKS = ["Qwen/Qwen3-32B", "meta-llama/Llama-3.1-8B-Instruct", "openai/gpt-oss-120b", "google/gemma-4-31B-it", "Qwen/Qwen3-235B-A22B-Instruct-2507", "deepseek-ai/DeepSeek-V3.2", "black-forest-labs/FLUX.1-dev", "openai/whisper-large-v3-turbo"];
+window.FASTGPU_CATALOG = [["Qwen/Qwen3-0.6B",0.75,"inference",31151426,null],["google/gemma-4-26B-A4B-it",25.8,"inference",12265732,null],["Qwen/Qwen3-8B",8.19,"inference",10122747,null],["BAAI/bge-large-en-v1.5",0.34,"embed",10098619,null],["Qwen/Qwen3-VL-8B-Instruct",8.77,"inference",9722655,null],["Qwen/Qwen3-4B",4.02,"inference",9603998,null],["google/gemma-4-31B-it",31.3,"inference",9565805,null],["Qwen/Qwen3-Embedding-0.6B",0.6,"embed",8981355,null],["Qwen/Qwen3.5-9B",9.65,"inference",8537820,null],["Qwen/Qwen2.5-7B-Instruct",7.62,"inference",8097130,null],["Qwen/Qwen3.5-4B",4.66,"inference",8082635,null],["meta-llama/Llama-3.2-1B-Instruct",1.24,"inference",7377126,null],["intfloat/multilingual-e5-large",0.56,"embed",7106424,null],["Qwen/Qwen3.8-27B",27.8,"inference",6841660,null],["Qwen/Qwen2.5-1.5B-Instruct",1.54,"inference",6730687,null],["openai/whisper-large-v3-turbo",0.81,"transcribe",6417171,null],["zai-org/GLM-5.3-Flash",321.3,"inference",6352803,8],["RadixArk/Kimi-K3-DSpark",2.25,"inference",6255908,null],["meta-llama/Llama-3.1-8B-Instruct",8.03,"inference",6191157,null],["openai/gpt-oss-20b",20.9,"inference",6110199,4],["unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF",30.5,"inference",6065516,4],["Qwen/Qwen2.5-VL-7B-Instruct",8.29,"inference",5436064,null],["nvidia/Qwen3.6-35B-A3B-NVFP4",35.0,"inference",5255793,4],["ornith-ai/Ornith-1.5-9B-GGUF",9.2,"inference",5146756,4],["Qwen/Qwen3-14B",14.8,"inference",4801773,null],["dphn/dolphin-2.9.1-yi-1.5-34b",34.4,"inference",4725613,null],["Qwen/Qwen3.8-27B-FP8",27.8,"inference",4666226,8],["deepseek-ai/DeepSeek-V4-Flash-0731",304.2,"inference",4520575,8],["Qwen/Qwen3.6-35B-A3B-FP8",36.0,"inference",4455887,8],["google/gemma-4-E4B-it",8.0,"inference",4380008,null],["stabilityai/stable-diffusion-xl-base-1.0",2.57,"generate",4367759,null],["deepseek-ai/DeepSeek-V3.2",685.4,"inference",4360882,8],["prism-ml/Ternary-Bonsai-2-27B-gguf",26.9,"inference",4345410,4],["Qwen/Qwen2.5-3B-Instruct",3.09,"inference",4333852,null],["openai/gpt-oss-120b",116.8,"inference",4094755,4],["Qwen/Qwen3.5-2B",2.27,"inference",4031032,null],["openai/whisper-large-v3",1.54,"transcribe",3939211,null],["cdiamond/Qwen3.8-27B-iMatrix-NVFP4-MTP-GGUF",27.0,"inference",3788082,4],["ornith-ai/Ornith-1.5-35B-A3B-GGUF",35.5,"inference",3678963,4],["Qwen/Qwen3-32B",32.8,"inference",3660105,null],["Qwen/Qwen3-4B-Instruct-2507",4.02,"inference",3650593,null],["google/embeddinggemma-300m",0.3,"embed",3630456,null],["Qwen/Qwen3.6-35B-A3B",36.0,"inference",3537522,null],["ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF",176.9,"inference",3405442,4],["openai/whisper-small",0.24,"transcribe",3276589,null],["Qwen/Qwen3-VL-4B-Instruct",4.44,"inference",3194167,null],["google/gemma-3-1b-it",1.0,"inference",3135298,null],["Qwen/Qwen3-1.7B",2.03,"inference",3110028,null],["Qwen/Qwen3-14B-AWQ",14.8,"inference",3009602,4],["google/gemma-4-E2B-it",5.12,"inference",2994679,null],["Qwen/Qwen3-VL-2B-Instruct",2.13,"inference",2681368,null],["Qwen/Qwen2.5-7B-Instruct-AWQ",7.62,"inference",2608841,4],["MahmoudAshraf/mms-300m-1130-forced-aligner",0.32,"transcribe",2595602,null],["Qwen/Qwen3.5-0.8B",0.87,"inference",2538664,null],["CMSManhattan/JiRackUltra_1b",1.78,"inference",2536181,null],["ornith-ai/Ornith-1.0-9B-GGUF",8.95,"inference",2504971,4],["Qwen/Qwen3-Embedding-8B",7.57,"embed",2446285,null],["Qwen/Qwen3.6-27B-FP8",27.8,"inference",2445241,8],["BAAI/bge-reranker-large",0.56,"embed",2347978,null],["Qwen/Qwen3-8B-AWQ",8.19,"inference",2344734,4],["Qwen/Qwen3.6-27B",27.8,"inference",2323903,null],["Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",1.92,"inference",2316282,null],["Qwen/Qwen2.5-VL-3B-Instruct",3.75,"inference",2250774,null],["vikhyatk/moondream2",1.93,"inference",2244764,null],["Qwen/Qwen2.5-Coder-7B-Instruct",7.62,"inference",2126685,null],["deepseek-ai/DeepSeek-OCR",3.34,"inference",2083310,null],["mixedbread-ai/mxbai-embed-large-v1",0.34,"embed",1977251,null],["jinaai/jina-embeddings-v3",0.57,"embed",1969193,null],["nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",3.97,"inference",1932186,null],["mistralai/Voxtral-Mini-4B-Realtime-2602",4.43,"transcribe",1915975,null],["Qwen/Qwen3.5-27B",27.8,"inference",1898479,null],["handy-computer/nemotron-3.5-asr-streaming-0.6b-gguf",0.64,"transcribe",1896811,4],["ggml-org/Qwen3.8-27B-GGUF",26.9,"inference",1877970,4],["datalab-to/chandra-ocr-2",5.3,"inference",1863549,null],["Qwen/Qwen2.5-32B-Instruct",32.8,"inference",1818722,null],["Qwen/Qwen3-Embedding-4B",4.02,"embed",1804509,null],["handy-computer/parakeet-unified-en-0.6b-gguf",0.62,"transcribe",1775150,4],["farbodtavakkoli/OTel-2.0-LLM-31B-IT",31.3,"inference",1765420,null],["Qwen/Qwen3-VL-8B-Instruct-FP8",8.77,"inference",1760786,8],["peculiar-ragdoll/Tiel-Coder-35B-A3B-GGUF-MTP",35.0,"inference",1740208,4],["Qwen/Qwen2.5-14B-Instruct-AWQ",14.8,"inference",1708009,4],["google/gemma-4-12B-it",12.0,"inference",1689082,null],["Qwen/Qwen2.5-VL-7B-Instruct-AWQ",8.29,"inference",1671851,4],["Qwen/Qwen3.8-Flash-Next",180.0,"inference",1640938,n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index.html

@@ -1,19 +1,81 @@
 <!doctype html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
-			</p>
-		</div>
-	</body>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+  <title>VRAM & GPU Cost Calculator: what any Hugging Face model needs, priced live</title>
+  <meta name="description" content="Pick any Hugging Face model and see how much GPU memory it needs to run or fine-tune, and the cheapest GPU setups that hold it, priced live by FastGPU.">
+  <link rel="preconnect" href="https://fonts.googleapis.com">
+  <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+  <link href="https://fonts.googleapis.com/css2?family=Instrument+Sans:wght@400;500;600&family=JetBrains+Mono:wght@400;500&family=Schibsted+Grotesk:wght@600;700&display=swap" rel="stylesheet">
+  <link rel="stylesheet" href="style.css">
+</head>
+<body>
+  <header class="top">
+    <a class="brand" href="https://fastgpu.co/?utm_source=huggingface&amp;utm_medium=space&amp;utm_campaign=gpu-cost-calculator" target="_blank" rel="noopener">
+      <svg viewBox="0 0 128 128" width="28" height="28" aria-hidden="true"><rect width="128" height="128" rx="30" class="mark-bg"/><path d="M35 32 L93 32 L83 50 L53 50 L53 56 L75 56 L65 74 L53 74 L53 96 L35 96 Z" class="mark"/></svg>
+      <span>FastGPU</span>
+    </a>
+    <span class="live"><span class="dot" aria-hidden="true"></span>Live GPU prices</span>
+  </header>
+
+  <main>
+    <h1>What GPU does this model need, and what does it cost to rent right now?</h1>
+    <p class="lede">Pick any Hugging Face model. You get the GPU memory it needs and the cheapest GPU setups that hold it, priced live across the clouds FastGPU tracks.</p>
+
+    <form id="calc" class="panel" autocomplete="off">
+      <label class="field-label" for="model">Model</label>
+      <div class="combo">
+        <input id="model" name="model" type="text" role="combobox" aria-autocomplete="list" aria-expanded="false" aria-controls="suggestions" spellcheck="false" placeholder="Search models, paste org/name, or type a size like 70B…">
+        <ul id="suggestions" role="listbox" aria-label="Models" hidden></ul>
+      </div>
+      <div id="picks" class="picks" aria-label="Popular models"></div>
+
+      <div class="opts">
+        <label>Job
+          <select id="task" name="task">
+            <option value="inference">Run / serve it</option>
+            <option value="finetune-lora">Fine-tune with LoRA</option>
+            <option value="finetune-full">Full fine-tune</option>
+            <option value="generate">Generate images or video</option>
+            <option value="transcribe">Transcribe speech</option>
+            <option value="embed">Embed text</option>
+          </select>
+        </label>
+        <label>Weights
+          <select id="precision" name="precision">
+            <option value="auto">As published</option>
+            <option value="fp16">16-bit (BF16 / FP16)</option>
+            <option value="int8">8-bit (FP8 / INT8)</option>
+            <option value="int4">4-bit (INT4 / AWQ / GGUF Q4)</option>
+          </select>
+        </label>
+        <label>Region
+          <select id="region" name="region">
+            <option value="">Anywhere</option>
+            <option value="US">US</option>
+            <option value="EU">EU</option>
+            <option value="ASIA">Asia</option>
+          </select>
+        </label>
+        <label class="check"><input id="spot" name="spot" type="checkbox"> Include spot (interruptible) capacity</label>
+      </div>
+    </form>
+
+    <p id="status" class="status" role="status"></p>
+    <section id="result" class="result" aria-live="polite"></section>
+  </main>
+
+  <footer>
+    <p>Sizing and prices come from <a href="https://fastgpu.co/?utm_source=huggingface&amp;utm_medium=space&amp;utm_campaign=gpu-cost-calculator" target="_blank" rel="noopener">FastGPU</a>, which compares live GPU rental prices across marketplaces, neoclouds and hyperscalers. Model facts come from the Hugging Face Hub.</p>
+    <p class="links">
+      <a href="https://huggingface.co/datasets/fastgpu/cloud-gpu-prices" target="_blank" rel="noopener">Open price dataset (CC BY 4.0)</a>
+      <a href="https://fastgpu.co/docs?utm_source=huggingface&amp;utm_medium=space&amp;utm_campaign=gpu-cost-calculator" target="_blank" rel="noopener">Free API</a>
+      <a href="https://fastgpu.co/methodology?utm_source=huggingface&amp;utm_medium=space&amp;utm_campaign=gpu-cost-calculator" target="_blank" rel="noopener">Method</a>
+    </p>
+  </footer>
+
+  <script src="catalog.js"></script>
+  <script src="app.js"></script>
+</body>
 </html>

style.css

@@ -1,28 +1,174 @@
+/* FastGPU brand: blue #2684FF, Schibsted Grotesk (display), Instrument Sans (text), JetBrains Mono (figures). */
+:root {
+  --brand: #2684ff;
+  --brand-ink: #0b5fd6;
+  --brand-soft: rgba(38, 132, 255, 0.1);
+  --bg: #f7f9fc;
+  --panel: #ffffff;
+  --ink: #0f1b2d;
+  --muted: #5a6b82;
+  --line: #dfe6ef;
+  --good: #11845b;
+  --radius: 10px;
+  --shadow: 0 1px 2px rgba(15, 27, 45, 0.06), 0 4px 16px rgba(15, 27, 45, 0.05);
+  color-scheme: light;
+}
+@media (prefers-color-scheme: dark) {
+  :root {
+    --brand-ink: #7fb6ff;
+    --brand-soft: rgba(38, 132, 255, 0.16);
+    --bg: #0b111b;
+    --panel: #111a28;
+    --ink: #e8eef7;
+    --muted: #93a3ba;
+    --line: #223147;
+    --good: #3ccf91;
+    --shadow: none;
+    color-scheme: dark;
+  }
+}
+
+* { box-sizing: border-box; }
+html { -webkit-text-size-adjust: 100%; }
 body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
+  margin: 0;
+  background: var(--bg);
+  color: var(--ink);
+  font: 400 15px/1.55 "Instrument Sans", system-ui, -apple-system, "Segoe UI", sans-serif;
 }
+a { color: var(--brand-ink); text-underline-offset: 2px; }
+a:focus-visible, button:focus-visible, input:focus-visible, select:focus-visible {
+  outline: 2px solid var(--brand);
+  outline-offset: 2px;
+}
+.mono, .num, .price { font-family: "JetBrains Mono", ui-monospace, SFMono-Regular, Menlo, monospace; font-variant-numeric: tabular-nums; }
+
+.top, main, footer { width: 100%; max-width: 920px; margin: 0 auto; padding: 0 20px; }
+.top { display: flex; align-items: center; justify-content: space-between; padding-top: 18px; }
+.brand { display: inline-flex; align-items: center; gap: 8px; color: var(--ink); text-decoration: none; font: 700 17px/1 "Schibsted Grotesk", system-ui, sans-serif; letter-spacing: -0.01em; }
+.mark-bg { fill: var(--brand); fill-opacity: 0.1; }
+.mark { fill: var(--brand); }
+.live { display: inline-flex; align-items: center; gap: 6px; font-size: 13px; color: var(--muted); }
+.dot { width: 8px; height: 8px; border-radius: 50%; background: var(--good); box-shadow: 0 0 0 3px color-mix(in srgb, var(--good) 22%, transparent); }
 
 h1 {
-	font-size: 16px;
-	margin-top: 0;
+  margin: 26px 0 8px;
+  font: 700 clamp(24px, 4vw, 34px)/1.15 "Schibsted Grotesk", system-ui, sans-serif;
+  letter-spacing: -0.02em;
+  text-wrap: balance;
+}
+.lede { margin: 0 0 20px; color: var(--muted); max-width: 62ch; }
+
+.panel {
+  background: var(--panel);
+  border: 1px solid var(--line);
+  border-radius: var(--radius);
+  box-shadow: var(--shadow);
+  padding: 16px;
+}
+.field-label { display: block; font-weight: 600; font-size: 13px; margin-bottom: 6px; }
+.combo { position: relative; }
+#model {
+  width: 100%;
+  font: inherit;
+  font-size: 16px;
+  color: var(--ink);
+  background: var(--bg);
+  border: 1px solid var(--line);
+  border-radius: 8px;
+  padding: 11px 12px;
+}
+#model::placeholder { color: var(--muted); }
+#suggestions {
+  position: absolute; z-index: 5; left: 0; right: 0; top: calc(100% + 4px);
+  margin: 0; padding: 4px; list-style: none;
+  background: var(--panel); border: 1px solid var(--line); border-radius: 8px; box-shadow: var(--shadow);
+  max-height: 340px; overflow-y: auto;
+}
+#suggestions li {
+  display: flex; justify-content: space-between; gap: 12px; align-items: baseline;
+  padding: 8px 10px; border-radius: 6px; cursor: pointer;
 }
+#suggestions li[aria-selected="true"], #suggestions li:hover { background: var(--brand-soft); }
+.opt-name { font-weight: 500; overflow-wrap: anywhere; }
+.opt-meta { color: var(--muted); font-size: 12.5px; white-space: nowrap; }
 
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
+.picks { display: flex; flex-wrap: wrap; gap: 6px; margin-top: 10px; }
+.chip {
+  font: 500 12.5px/1 "Instrument Sans", system-ui, sans-serif;
+  color: var(--ink); background: transparent;
+  border: 1px solid var(--line); border-radius: 999px; padding: 7px 10px; cursor: pointer;
 }
+.chip:hover { border-color: var(--brand); color: var(--brand-ink); }
 
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
+.opts { display: grid; grid-template-columns: repeat(3, minmax(0, 1fr)); gap: 12px; margin-top: 14px; }
+.opts label { display: grid; gap: 5px; font-weight: 600; font-size: 13px; min-width: 0; }
+.opts select {
+  width: 100%; min-width: 0;
+  font: 400 14px/1.3 "Instrument Sans", system-ui, sans-serif; color: var(--ink);
+  background: var(--bg); border: 1px solid var(--line); border-radius: 8px; padding: 8px 10px;
 }
+.opts .check { grid-column: 1 / -1; display: flex; align-items: center; gap: 8px; font-weight: 500; }
+.opts .check input { width: 16px; height: 16px; accent-color: var(--brand); }
+
+.status { min-height: 1.4em; margin: 14px 2px 0; color: var(--muted); font-size: 13.5px; }
+.result { margin-top: 6px; transition: opacity 0.15s ease; }
+.result.loading { opacity: 0.45; }
+@media (prefers-reduced-motion: reduce) { .result { transition: none; } }
+
+.model { margin: 4px 0 14px; }
+.model-id { font: 700 20px/1.25 "Schibsted Grotesk", system-ui, sans-serif; letter-spacing: -0.01em; overflow-wrap: anywhere; color: var(--ink); text-decoration: none; }
+a.model-id:hover { color: var(--brand-ink); text-decoration: underline; }
+.facts { margin: 4px 0 0; color: var(--muted); }
+.fineprint { margin: 6px 0 0; color: var(--muted); font-size: 13px; max-width: 78ch; }
+
+.need {
+  background: var(--panel); border: 1px solid var(--line); border-left: 4px solid var(--brand);
+  border-radius: var(--radius); padding: 14px 16px; margin-bottom: 18px;
+}
+.need-num { margin: 0; font: 700 34px/1.1 "Schibsted Grotesk", system-ui, sans-serif; letter-spacing: -0.02em; }
+.need-num .mono { font-weight: 500; }
+.need-what { margin: 2px 0 0; font-weight: 500; }
+
+.table-head { display: flex; align-items: end; justify-content: space-between; gap: 12px; flex-wrap: wrap; margin-bottom: 8px; }
+.table-head h2 { margin: 0; font: 700 17px/1.3 "Schibsted Grotesk", system-ui, sans-serif; }
+.sort { display: inline-flex; border: 1px solid var(--line); border-radius: 8px; overflow: hidden; }
+.sort button { font: 500 13px/1 "Instrument Sans", system-ui, sans-serif; color: var(--muted); background: var(--panel); border: 0; padding: 8px 11px; cursor: pointer; }
+.sort button[aria-pressed="true"] { background: var(--brand-soft); color: var(--brand-ink); }
+
+table { width: 100%; border-collapse: separate; border-spacing: 0; background: var(--panel); border: 1px solid var(--line); border-radius: var(--radius); overflow: hidden; }
+th, td { text-align: left; padding: 10px 12px; border-bottom: 1px solid var(--line); vertical-align: top; }
+thead th { font: 600 12px/1.2 "Instrument Sans", system-ui, sans-serif; text-transform: uppercase; letter-spacing: 0.04em; color: var(--muted); background: color-mix(in srgb, var(--bg) 60%, var(--panel)); }
+tbody tr:last-child th, tbody tr:last-child td { border-bottom: 0; }
+tbody th { font-weight: 500; }
+.num { text-align: right; white-space: nowrap; }
+.setup { display: block; font-weight: 600; }
+.sub { display: block; color: var(--muted); font: 400 12.5px/1.35 "Instrument Sans", system-ui, sans-serif; margin-top: 2px; }
+.price { font-weight: 500; text-decoration: underline dotted; text-decoration-thickness: 1px; }
+tr.cheapest .price { color: var(--good); }
+tr.cheapest .num .sub { color: var(--good); }
+.star { color: #c58a00; }
+
+.notice { background: var(--panel); border: 1px solid var(--line); border-radius: var(--radius); padding: 14px 16px; }
+.linklike { font: inherit; color: var(--brand-ink); background: none; border: 0; padding: 0; text-decoration: underline; cursor: pointer; }
+
+.cta { display: flex; flex-wrap: wrap; align-items: center; gap: 16px; margin: 16px 0 0; }
+.button { display: inline-block; background: var(--brand); color: #fff; text-decoration: none; font-weight: 600; padding: 10px 14px; border-radius: 8px; }
+.button:hover { background: #1a6fe0; }
+
+footer { margin-top: 36px; padding-top: 16px; padding-bottom: 28px; border-top: 1px solid var(--line); color: var(--muted); font-size: 13px; }
+footer p { margin: 0 0 8px; max-width: 78ch; }
+footer .links { display: flex; flex-wrap: wrap; gap: 16px; }
 
-.card p:last-child {
-	margin-bottom: 0;
+@media (max-width: 640px) {
+  .opts { grid-template-columns: 1fr 1fr; }
+  .opts label:nth-child(3) { grid-column: 1 / -1; }
+  table, thead, tbody, tr, th, td { display: block; }
+  thead { display: none; }
+  table { border: 0; background: transparent; }
+  tbody tr { background: var(--panel); border: 1px solid var(--line); border-radius: var(--radius); margin-bottom: 8px; padding: 6px 0; display: grid; grid-template-columns: 1fr auto; }
+  tbody th, tbody td { border: 0; padding: 4px 12px; }
+  tbody th { grid-column: 1 / -1; }
+  .num { text-align: left; }
+  tbody td:nth-of-type(2)::before { content: "per month "; color: var(--muted); font-size: 12px; font-family: "Instrument Sans", system-ui, sans-serif; }
 }