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SergiioB/intel-arc-pro-b70-inference-cookbook: Open recipes, engine patches, and benchmark harnesses for LLM inference on Intel Arc Pro B60/B70 (Battlemage, Xe2). MoE 35B at 160 t/s decode / 7.5K t/s prefill single-stream, 27B at 50~ t/s decode / 1.7

SergiioB/intel-arc-pro-b70-inference-cookbook: Open recipes, engine patches, and benchmark harnesses for LLM inference on Intel Arc Pro B60/B70 (Battlemage, Xe2). MoE 35B at 160 t/s decode / 7.5K t/s prefill single-stream, 27B at 50~ t/s decode / 1.7

12 hours ago

Intel Arc Pro B70 Inference Cookbook

Pinned vLLM XPU and llama.cpp SYCL recipes, failure notes, and measurement records for Intel Arc Pro B60/B70. Start with what worked, what did not, and what to test next. For a first local run, use START.md.

Pick one path

| Your goal | Start here | Status | |---|---|---| | Choose a model for a job | Model comparison | 94-task quality screen of Bolt 74B-A9B (two cards), Qwen3.8-27B and Qwen3.6-35B-A3B (one card each) with power and heat; one sample per task, Qwen3.6 partial. | | Serve on one B70 | Qwen3.8-27B EXL3 route | Primary route: trellis 4bpw + MTP3 + fp8 KV on the exl3xpu engine; the only measured route at full 262,144 context and clean on the exact-answer battery. The GPTQ/W4A16 route is legacy — that class fails the canary on this model. | | Run GGUF or vision | Muse-Glimmer llama.cpp route | Measured on its own engine and workload; no cross-engine speed ranking without matched timing. | | Run MoE or long context | Qwen3.6-35B-A3B recipe | Historical MTP speed record is stack-specific; a later matched comparison did not reproduce it. | | Serve on two B70s | Independent-card setup | Default: one server per card. FP8 TP2 is a research route, not an automatic single-request win. | | Diagnose or reproduce a number | Image and patch matrix → family recipe → benchmark catalog | Match image, patch, model, context, cache, power, and timing definition. |

Read the decision guide before tuning. It separates measured improvements, failed attempts, unpublished lab screens, and stop/go gates. A passing smoke canary does not establish model quality. No public performance claim from a different model, engine, host, or result generation transfers to this setup.

Reproduce and operate

  1. Check your card and host with scripts/doctor.py, then use the quick start for a pinned single-card run.
  2. Check the image and ordered patch matrix for the exact family before launching. Do not mix family patch stacks.
  3. Connect a client using client setup; use the reliability guide if the server hangs.
  4. Confirm a recipe with the reproduction procedure and benchmark format. The numeric authority is data/benchmarks.v1.json; docs/BENCHMARK-CATALOG.md is generated.
More routes: Nemotron DFlash, Nemotron Bolt 74B (one card on llama.cpp, or two cards on vLLM), Flash-Next, Ornith-1.5-122B GGUF, Windows, and family-specific research. Research routes are not interchangeable with the first-run recipe.

Quantize a model yourself: how the Bolt 74B AutoRound W4A16 quant was made and checked on two B70s, with every command, fix and script.

Request or add a recipe

Need a model or configuration not yet covered? Submit a recipe request with your target Hugging Face model and hardware setup.

To contribute a recipe directly to the B70 Launcher catalog, follow the recipe format specification to edit recipes.json, verify with python3 -m unittest tests.test_recipes, and open a pull request.

For contributors and agents

Use data/system-map.v1.json to select an intent. docs/AGENT-SYSTEM.md explains the authority layers; docs/ADDING-A-RECIPE.md explains publication. Keep historical evidence intact. Validate changes with python3 scripts/check-repo.py.

Code: MIT. Measurement reports and prose: CC BY 4.0. See LICENSE.

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