Instructions to use SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL # Run inference directly in the terminal: llama cli -hf SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL # Run inference directly in the terminal: llama cli -hf SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL # Run inference directly in the terminal: ./llama-cli -hf SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL
Use Docker
docker model run hf.co/SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL
- LM Studio
- Jan
- Ollama
How to use SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF with Ollama:
ollama run hf.co/SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL
- Unsloth Desktop
- Pi
How to use SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF with Docker Model Runner:
docker model run hf.co/SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL
- Lemonade
How to use SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL
Run and chat with the model
lemonade run user.Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF-Q3_K_XL
List all available models
lemonade list
- Hermes Agent
How to use SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "SixVolts/Qwen3.5-122B-A10B-Opus-Reasoning-MTP-GGUF:Q3_K_XL" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Qwen3.5-122B-A10B + MTP on AMD Strix Halo (27.8 tok/s @ ~23k context)
I've been benchmarking Qwen3.5-122B-A10B-Opus-Reasoning-Q4_K_XL + MTP on an AMD Ryzen AI MAX+ 395 (Strix Halo, 128 GB LPDDR5X, ~256 GB/s UMA) using the latest llama.cpp (llama-server build 10205 with ROCm).
After testing multiple combinations my overall best results:
llama-server \
-m Qwen3.5-122B-A10B-Opus-Reasoning-Q4_K_XL.gguf \
--model-draft mtp-opus-q4kxl-draft.gguf \
--spec-type draft-mtp \
--spec-draft-n-max 2 \
--spec-draft-ngl all \
-ngl 999 \
-fa on \
-lm none \
-c 262144 \
--batch-size 8192 \
--ubatch-size 4096 \
--threads 16 \
--threads-batch 16 \
--parallel 1 \
--cache-type-k q8_0 \
--cache-type-v q8_0 \
--cache-ram 32768 \
--jinja \
--chat-template-file chat_template.jinja
Results:
- Generation: ~27.6–27.9 tokens/s
- MTP draft acceptance: ~91%
Actual context during the benchmark: ~23k tokens
Chat template: https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates/resolve/main/chat_template.jinja
I'd be interested to compare results with other Strix Halo owners, especially if anyone has found additional optimizations for ROCm or llama.cpp.
679 tok/s prefill · 37 tok/s decode - 64k context using Vulkan, Strix Halo 128GB (framework desktop board)
/home/sixvolts/llama-b10066-vulkan/llama-b10066/llama-server
-m /home/sixvolts/models/qwen-3.5-122B-opus-mtp/Qwen3.5-122B-A10B-Opus-Reasoning-Q4_K_XL.gguf
-md /home/sixvolts/models/qwen-3.5-122B-opus-mtp/mtp-opus-q4kxl-draft.gguf
--spec-type draft-mtp --spec-draft-n-max 2 --spec-draft-n-min 1
-ngl 99 -fa on --no-mmap
--parallel 6 -c 393216 --no-kv-unified
-ctk q8_0 -ctv q8_0 -ctkd q8_0 -ctvd q8_0
-t 12 -b 2048 -ub 512
--jinja
--host 0.0.0.0 --port 8080
--alias qwen3.5-122b-opus