Building Cost-Efficient Enterprise RAG applications with Intel Gaudi 2 and Intel Xeon
Hugging Face
Intel and Hugging Face built a full RAG chatbot stack using Gaudi 2 chips and Xeon CPUs instead of Nvidia gear. It's slower than H100 but way cheaper per dollar, which is the pitch enterprises actually care about.
Based on reporting by Hugging Face — read the original for the full story.
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Intel just published a detailed blueprint for building retrieval-augmented generation apps entirely on its own silicon, and the interesting part isn't the architecture — it's the price tag. The setup pairs Gaudi 2 accelerators for running the language model with Granite Rapids Xeon CPUs handling embeddings and the Redis vector database, all stitched together with LangChain's rag-redis template and Hugging Face's Optimum Habana library.
The demo itself is fairly standard RAG fare: Nike's public 10-K financial filings get chunked, embedded with BAAI/bge-base-en-v1.5, and stashed in Redis. Queries then hit a retriever running in max marginal relevance mode, get stuffed into a prompt template, and get shipped off to a Llama2-70B or Intel NeuralChat model served through Hugging Face's Text Generation Inference server on Gaudi 2. Nothing revolutionary about the pipeline. What Intel is really selling here is the hardware underneath it.
And the benchmark numbers back up the pitch. Nvidia's H100 setup edged out four Gaudi 2 cards by 1.13x on raw throughput running Llama2-70B with 16 concurrent users. But when Intel factored in cost using MosaicML's published pricing methodology from January 2024, Gaudi 2 delivered more than double the performance per dollar — H100 landed at just 0.44x by comparison. That's a big gap for anyone budgeting a production deployment rather than chasing a leaderboard.
Intel also points to FP8 quantization as a lever still on the table, claiming roughly 1.8x throughput gains over the default bfloat16 setup on Gaudi 2, plus optional content moderation through Meta's Llama Guard model if enterprises want a safety layer bolted on. All of this ships as part of OPEA, Intel's open enterprise AI platform, with Dockerized environments meant to get developers from zero to a running chatbot without wrestling with driver installs or dependency hell.
The broader message is less about beating Nvidia at raw speed and more about total cost of ownership — a phrase Intel repeats deliberately. For enterprises running RAG at scale, where you're paying for every GPU-hour and every accelerator card, a 13% throughput deficit that comes with roughly half the cost per unit of performance is a trade a lot of CFOs would take without blinking.
My take — AI-written commentary, not fact-checked reporting
I like that Intel is pricing against Nvidia head-on instead of hiding behind vague ‘efficiency' claims — that performance-per-dollar number is exactly the metric enterprises should be demanding from every AI vendor, and too few publish it. Whether Gaudi 2 actually displaces H100 in production depends less on this benchmark and more on whether Intel's software tooling stays this smooth outside a curated demo, which is historically where Intel's AI story has stumbled.
Read more about this at: Hugging Face