🚀 Accelerating LLM Inference with TGI on Intel Gaudi
Hugging Face
Hugging Face just folded Intel Gaudi support straight into TGI, ditching the old separate fork. Means you can now run Llama, Mixtral and friends on non-Nvidia chips without babysitting a custom repo.
Based on reporting by Hugging Face — read the original for the full story.
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Hugging Face has merged Intel Gaudi support directly into Text Generation Inference, its main serving stack for large language models. Until now, Gaudi users had to rely on a separate fork called tgi-gaudi, which lagged behind the main branch and made keeping up with new TGI features a chore. That fork is effectively retired. Thanks to TGI's new multi-backend architecture, Gaudi now sits alongside other hardware targets in the same codebase, via a change tracked as PR #3091.
The move covers Intel's entire Gaudi lineup. Gaudi1 chips run on AWS EC2 DL1 instances, Gaudi2 shows up on Intel Tiber AI Cloud and Denvr Dataworks, and Gaudi3 is available through Intel Tiber AI Cloud, IBM Cloud, and OEM partners like Dell, HP and Supermicro. That's a real spread of cloud and on-prem options for anyone who wants an alternative to the usual GPU suppliers.
On the practical side, TGI's production features — dynamic batching, streamed responses, multi-card sharding — now work on Gaudi out of the box. Hugging Face has specifically tuned the modeling code for models like Llama 3.1 and 3.3, Mixtral 8x7B, Falcon 180B, Qwen2 72B, Gemma 7B, and vision models such as Llama 3.2 Vision and Llava-v1.6-Mistral-7B. FP8 quantization is also supported through Intel's Neural Compressor, which should squeeze more throughput out of the hardware for teams watching cost per token.
Getting started is a matter of pulling the official Docker image and running it on Gaudi hardware with the habana runtime flag, pointing at a model like Meta-Llama-3.1-8B-Instruct. Hugging Face says DeepSeek-r1/v3 and Qwen-VL support are coming next, and it's asking the community to test the backend and file feedback or contributions on GitHub.
My take — AI-written commentary, not fact-checked reporting
This is exactly the kind of infrastructure work that doesn't trend on social media but quietly matters more than another benchmark chart. Nvidia's grip on inference is less about raw silicon superiority and more about tooling lock-in, and every time TGI treats Gaudi as a first-class citizen instead of a bolted-on fork, that grip loosens a little. I'd like to see more European and open-source shops actually benchmark this against H100 pricing before assuming Nvidia is the only sane default.
Read more about this at: Hugging Face