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README-model-card.md

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+---
+license: mit
+base_model: deepseek-ai/DeepSeek-V4.1-Flash
+base_model_relation: quantized
+pipeline_tag: text-generation
+tags:
+- deepseek
+- deepseek-v4.1
+- nvfp4
+- modelopt
+- vllm
+- blackwell
+- quantized
+- atomic-chat
+---
+
+# DeepSeek-V4.1-Flash-NVFP4
+
+[deepseek-ai/DeepSeek-V4.1-Flash](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash) with its routed experts
+re-encoded as NVFP4, calibrated by NVIDIA's DeepSeek-V4 recipe, and **measured against the original**. Everything
+below was produced by one run of [`foundry-nvfp4.sh`](https://github.com/AtomicBot-ai/atomic-quantizer/blob/main/scripts/foundry-nvfp4.sh)
+on a 4×B200 box on 2026-09-10; the logs and the raw measurements are in
+[AtomicChat/DeepSeek-V4.1-Flash-NVFP4-metrics](https://huggingface.co/datasets/AtomicChat/DeepSeek-V4.1-Flash-NVFP4-metrics).
+
+## What this is, and what it is not
+
+V4.1-Flash ships its routed experts in MXFP4 already. NVFP4 keeps every nibble and rewrites the block scales
+(E4M3 per 16 plus one fp32 per tensor instead of E8M0 per 32). The cast is lossless: **16,986,931,200 of
+16,986,931,200 blocks** transcoded bit-exactly. Engram tables, attention, shared experts, the LM head and the
+MTP/DSpark draft stay in their original FP8 / BF16. The checkpoint is 8 GiB larger than the original, not smaller.
+
+What NVIDIA's recipe adds is one calibrated `input_scale` per expert projection: it places the E4M3 window for the
+FP4 activations on Blackwell's W4A4 NVFP4 MoE kernels. Calibration: `cnn_dailymail` + `nvidia/Nemotron-Post-Training-Dataset-v2`,
+64 samples of 512 tokens each, through DeepSeek's reference `inference/model.py` under
+[NVIDIA Model-Optimizer](https://github.com/NVIDIA/Model-Optimizer) at commit `079078de` with two one-line patches for V4.1
+(tokenizer into the Transformer, FP8 block size read from the scale shape). 15,246 of 15,360 routed experts (99.3 %)
+received calibration tokens; the rest got the per-layer fallback scale.
+
+This checkpoint does **not** make the model faster on Hopper (NVFP4 runs through Marlin there and the calibrated
+scales are ignored) and it is not smaller than the original. What it offers is the NVFP4 W4A4 path on Blackwell,
+with numbers instead of a promise.
+
+## Measured against the original
+
+Reference: the original MXFP4 checkpoint served by the same vLLM build on the same 4×B200. Protocol: windows of
+4096 tokens, second half scored, 24 windows per corpus, 49,152 scored positions each, top-512 log probabilities
+(the KLD is a bracket; the numbers below are its lower edge, the upper edge is within 0.0004 on every row).
+`ref-repeat` is the original scored twice and is the noise floor of the stand: MoE routing on non-deterministic
+kernels flips 2–4 % of top-1 tokens between two runs of the same model.
+
+| corpus | build | mean KLD | median KLD | p99 KLD | top-1 agree | ppl |
+| --- | --- | --- | --- | --- | --- | --- |
+| neutral (30 languages) | **this checkpoint** | 0.0353 | 0.00277 | 0.458 | 94.12 % | 2.9950 |
+| | cast, no calibration | 0.0344 | 0.00276 | 0.433 | 94.39 % | 2.9928 |
+| | original vs itself (noise) | 0.0159 | 0.00131 | 0.205 | 96.07 % | 2.9677 |
+| code | **this checkpoint** | 0.0199 | 0.000037 | 0.316 | 96.76 % | 1.8955 |
+| | cast, no calibration | 0.0191 | 0.000036 | 0.296 | 96.84 % | 1.9013 |
+| | original vs itself (noise) | 0.0101 | 0.000022 | 0.160 | 97.69 % | 1.8895 |
+| agentic | **this checkpoint** | 0.0089 | 0.000006 | 0.128 | 98.32 % | 1.3868 |
+| | cast, no calibration | 0.0085 | 0.000005 | 0.127 | 98.40 % | 1.3871 |
+| | original vs itself (noise) | 0.0055 | 0.000004 | 0.083 | 98.63 % | 1.3846 |
+
+Two honest readings of this table:
+
+1. **The calibrated checkpoint and a plain cast with `input_scale = 1.0` are indistinguishable** within the noise of
+   the stand: medians agree to the sixth decimal, mean KLD differs by 0.001 against a floor of 0.016. For this
+   model that is expected: DeepSeek trained it with SwiGLU clamps that bound the expert inputs at 189 and 150, and
+   the flat window already covers that range at full scale precision. The calibration is what NVIDIA's recipe
+   prescribes, and it costs nothing, but it does not buy anything measurable here.
+2. **The cost of W4A4 itself is real and small**: about 1.7 points of top-1 agreement beyond the noise on general
+   text, 0.8 % perplexity on text, 0.5 % on code, 0.2 % on agentic dialogue, relative to the original served
+   natively. The original MXFP4 path does not pay this.
+
+Speed is not measured and not claimed.
+
+## Running it
+
+Needs Blackwell (B200/B300/GB200/GB300, or RTX PRO 6000) and a vLLM that knows `deepseek_v41`. As of 2026-09-10
+that is branch `dsv41-feat` of vllm-project/vllm ([PR #56214](https://github.com/vllm-project/vllm/pull/56214)),
+built from source at `e47aa780`; the loader keys on `moe_quant_algo: NVFP4` in `config.json` and routes the experts
+to the FlashInfer TRT-LLM NVFP4 MoE backend. Measured with `tensor_parallel_size=4`, `max_model_len=4097`, no
+speculative decoding: the DSpark draft experts are left in MXFP4 and that combination has not been exercised.
+
+## Reproduce
+
+Runbook, toolbox and the exact commands: [atomic-quantizer/docs/runbook-nvfp4.md](https://github.com/AtomicBot-ai/atomic-quantizer/blob/main/docs/runbook-nvfp4.md).
+The upstream reference code, encoding and license files are DeepSeek's, copied unchanged.