Gaudi Cookbook¶
Practical, tested ways to serve open LLMs with vLLM on Intel Gaudi 2, through the vllm-gaudi plugin.
Everything here was run on a large Gaudi 2 fleet serving real traffic, and everything uses public images and public checkpoints.
New: MXFP4 MoE decode kernels
Up to 2.5x faster decode for MXFP4 mixture-of-experts models on one 8-card Gaudi 2 node: GLM-5.3 single stream from 23.3 to 59.1 tokens/s, Kimi-K2.7 from 17.9 to 44.5, Muse Spark Open 2.4x aggregate at four streams, with lower numerical error than the stock op. Read the write-up.
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Copy-paste
vllm servecommands per model: cards, precision, buckets, parsers, the checks that passed and the pitfalls to avoid. -
Small runtime fixes for bugs in released vllm-gaudi images, each linked to its upstream pull request. They refuse to start if the image does not match.
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MXFP4 MoE decode kernels that read the packed weights directly: up to 2.5x faster decode on GLM-5.x, Kimi-K2.7, Muse Spark Open and DeepSeek-V4.1-Flash.
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Seven models that released images cannot serve, brought up on Gaudi 2: Muse Spark Open, GLM-5.3-Flash, DeepSeek-V4.1-Flash, Beam-1.0 and more.
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Platform findings, prefix caching on hybrids, MoE decode-step anatomy, two-node pipeline parallel, fast prefill, speech and embeddings.
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Running Gaudi at fleet scale: pod spec, bin-pack scheduling, warm-up, health, rollouts, autoscaling and routing.
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Point Claude Code, Codex, Cursor or another coding agent at this repository.
AGENTS.mdtells it where everything is and what it must not do.
Start here¶
- Check the software baseline and build or pull a matching image.
- Pick a recipe and apply its required fixes.
- Moving to Kubernetes? Read the pod spec page before anything else.
- Working with a coding agent? Point it at this repository; see Use with an agent.
Status¶
This is an independent project. It is not affiliated with Intel or the vLLM project. Fixes and kernels here are offered upstream first; when a change ships in a vllm-gaudi release, the matching fix is marked as superseded.