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The world of Large Language Models (LLMs) is evolving rapidly, and efficient inference is crucial for real-world applications. Text Generation Inference (TGI), a high-performance serving solution for LLMs, now fully supports Intel Gaudi AI accelerators. This integration enables developers and enterprises to leverage Gaudi’s hardware capabilities seamlessly, improving deployment efficiency and performance while expanding beyond traditional GPU-based solutions.
With this native support, Intel Gaudi-powered inference becomes easier to use, more accessible, and optimized for key AI workloads. Let’s explore what this integration brings and why it matters.
What’s New?
TGI has now fully integrated Gaudi support into its main codebase (PR 3091), eliminating the need for a separate Gaudi fork. Previously, Gaudi users had to rely on a custom repository (tgi-gaudi), which led to compatibility issues and delayed feature rollouts. The new multi-backend architecture allows Gaudi devices to be natively supported, ensuring smoother adoption and upgrades.
Gaudi Hardware Support
Intel’s full range of Gaudi AI accelerators is now compatible with TGI:
- Gaudi1 💻 – Available on AWS EC2 DL1 instances
- Gaudi2 💻💻 – Available on Intel Tiber AI Cloud and Denvr Dataworks
- Gaudi3 💻💻💻 – Found on Intel Tiber AI Cloud, IBM Cloud, and OEMs like Dell, HP, and Supermicro
For more details, check
Why This Matters
Key Benefits of Gaudi Integration in TGI
- More Hardware Choices 🔄 – Expands LLM deployment options beyond traditional GPUs.
- Cost-Effective Solutions 💰 – Gaudi hardware provides competitive price-performance ratios for AI workloads.
- Production-Ready ⚙️ – Features such as dynamic batching and streaming responses are fully functional on Gaudi.
- Broad Model Support 🤖 – Run popular models like Llama 3.1, Mixtral, and Mistral on Gaudi hardware.
- Advanced AI Features 🔥 – Enables multi-card inference (sharding), vision-language models, and FP8 precision for enhanced performance.
Getting Started with TGI on Gaudi
To run TGI on Gaudi, use the official Docker image on a Gaudi-equipped machine:
“`bash
model=meta-llama/Meta-Llama-3.1-8B-Instruct
volume=$PWD/data
hf_token=YOUR_HF_ACCESS_TOKEN
docker run –runtime=habana –cap-add=sys_nice –ipc=host
-p 8080:80
-v $volume:/data
HF_TOKEN=$hf_token
HABANA_VISIBLE_DEVICES=all
ghcr.io/huggingface/text-generation-inference:3.2.1-gaudi
–model-id $model
“`
After launching the server, inference requests can be sent via:
“`bash
curl 127.0.0.1:8080/generate
-X POST
-d {“inputs”:”What is Deep Learning?”,”parameters”:{“max_new_tokens”:32}}
-H Content-Type: application/json
“`
For detailed setup instructions and advanced configurations, check the official TGI Gaudi backend documentation.
Optimized Model Performance
Intel Gaudi hardware has been optimized for both single and multi-card configurations, ensuring maximum performance for the following models:
– Llama 3.1 (8B, 70B)
– Llama 3.3 (70B)
– Llama 3.2 Vision (11B)
– Mistral (7B)
– Mixtral (8×7B)
– CodeLlama (13B)
– Falcon (180B)
– Qwen2 (72B)
– Starcoder & Starcoder2
– Gemma (7B)
– Llava-v1.6-Mistral-7B
– Phi-2
Upcoming Features
Intel Gaudi support is continuously evolving. Future updates will include models like DeepSeek-r1/v3, QWen-VL, and other next-gen LLMs to further enhance AI capabilities.
Community Involvement
The TGI team welcomes contributions and feedback. Developers can explore documentation, contribute via GitHub, and provide insights to improve the system. By integrating Gaudi support, TGI aims to make LLM deployments more flexible and efficient.
What Undercode Say:
Intel Gaudi vs. Traditional GPUs
The AI industry has been heavily reliant on GPUs, primarily from NVIDIA. However, Intel Gaudi presents a viable alternative, offering:
- Competitive Performance – Optimized for AI inference with robust parallel processing.
- Cost Benefits – Often provides lower costs for certain workloads compared to GPUs.
- Scalability – Supports multi-card configurations for large-scale deployments.
Market Impact of TGI’s Gaudi Integration
With Hugging Face integrating Gaudi directly into TGI, the open-source AI ecosystem gains:
- Broader Hardware Support – Expanding AI model deployment beyond proprietary GPU ecosystems.
- Open-Source Innovation – Encouraging competition and diversity in AI hardware.
- Enterprise Adoption – Companies looking for cost-effective inference solutions may increasingly adopt Gaudi.
Performance and Efficiency Gains
The FP8 precision and advanced inference techniques available in Gaudi offer:
- Lower Power Consumption – Efficient computation reduces energy costs.
- Faster Processing – Optimized LLM inference speeds up response times.
- Better Model Utilization – Multi-card sharding enhances parallelism for high-demand workloads.
Challenges and Considerations
Despite its advantages, Intel Gaudi faces hurdles:
– Software Ecosystem –
- Adoption Rate – The market’s reliance on established GPU solutions slows down transition.
- Vendor Lock-In Risks – Cloud providers offering Gaudi may create ecosystem-specific dependencies.
The Future of AI Hardware
TGI’s Gaudi integration signals a shift towards diversified AI infrastructure. As alternative AI accelerators gain traction, expect:
- More Competition – Intel, AMD, and other vendors will challenge NVIDIA’s dominance.
- Enhanced AI Accessibility – Open-source solutions will drive affordability and adoption.
- Specialized AI Chips – The rise of domain-specific hardware for optimized AI workloads.
With Gaudi now a native part of TGI, developers have more choices for deploying high-performance LLMs without being locked into a single vendor.
Fact Checker Results
- TGI’s Gaudi support is officially integrated – Confirmed via Hugging Face’s PR 3091.
- Gaudi’s AI accelerators are commercially available – Verified on AWS, Intel Tiber AI Cloud, and other platforms.
- Performance claims align with benchmarks – Gaudi hardware has shown strong inference performance in AI tasks.
References:
Reported By: https://huggingface.co/blog/intel-gaudi-backend-for-tgi
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