Hugging Face has released a dedicated text-generation pipeline for Intel’s Gaudi 2 AI accelerators via Optimum Habana version 1.10.4, giving developers a high-level abstraction that handles the full end-to-end workflow — pre-processing, inference, and post-processing — without writing boilerplate code. The pipeline lives in the Optimum Habana repository and can be invoked three ways: through a run_pipeline.py script, embedded directly in Python, or fed into LangChain classes for retrieval-augmented generation setups.
Getting started requires jumping through Meta’s gated access hoops for Llama 2. Developers need to accept terms on the Meta website, wait a day or two for approval, then request access on Hugging Face using the same email. After that, a huggingface-cli login with an access token gets you in. The install is straightforward: pip install optimum-habana==1.10.4, clone the v1.10-release branch, and you’re ready. For distributed inference on larger models, DeepSpeed is needed — the article specifies SynapseAI 1.14.0 with a matching DeepSpeed fork from HabanaAI’s GitHub.
The pipeline supports a range of Llama 2 sizes, from 7B to 70B parameters. Sample commands show how to pass single or multiple prompts, adjust temperature and top_p values, and enable HPU graphs and KV caching for performance. For the 70B model, the article demonstrates launching with gaudi_spawn.py across 8 workers using DeepSpeed and bf16 precision — a meaningful signal that Gaudi 2 can handle the largest Llama 2 variant in a distributed configuration.
What’s notable here is the LangChain integration. By passing use_with_langchain as a constructor argument, developers can drop the Gaudi-accelerated pipeline directly into LangChain’s HuggingFacePipeline wrapper, PromptTemplate, and LLMChain classes. That opens the door to running RAG applications and prompt-templated workflows on Gaudi 2 hardware without leaving the LangChain ecosystem. It’s a smart move by Hugging Face to meet developers where they already are, rather than asking them to learn a new inference framework.
