Gaudi 3 128GB

Gaudi 3 128GB (128 GB VRAM) can run these top models: Qwen 3.5 122B A10B (score: 99/100), Mistral Small 4 119B (score: 97/100), Devstral 2 123B Instruct (score: 97/100). See the full compatibility list above.

Gaudi 3 128GB has 128 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.

Yes, Gaudi 3 128GB is excellent for running LLMs locally with top compatibility scores above 80/100.

For coding on Gaudi 3 128GB, we recommend Qwen3-Coder-Next. It achieves 109.7 tokens per second with 256K context window. Qwen3-Coder-Next is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.

There are 4 upgrade path(s) from Gaudi 3 128GB: Gaudi 3 128GB, NVIDIA H200 141GB. Upgrading would unlock larger models and faster inference speeds.

Yes, Gaudi 3 128GB with 128 GB of usable memory can run Flux.1 Dev at FP16 natively. Flux is a 12B parameter diffusion transformer that produces high-quality images. You can also run the Schnell variant for faster generation.

Gaudi 3 128GB (128 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. Flux.1 Dev also runs natively for state-of-the-art image quality. For video, LTX Video 2.3 can generate short clips. Check the AI Capability Matrix above for detailed compatibility.

Gaudi 3 128GB is excellent for AI image generation. With 128 GB of usable memory, it runs all major diffusion models including Flux.1, SDXL, and Stable Diffusion 3.5 at full precision. You can generate high-resolution images quickly and even handle video generation models.

Yes, Gaudi 3 128GB with 128 GB of usable memory can run Qwen 3.5 27B at Q8 (near-lossless, ~28.9 GB) or even FP16 (~55.4 GB) depending on your context needs. This setup provides an excellent experience with this model. Use Ollama or vLLM for best results.

With 128 GB VRAM on Gaudi 3 128GB, use Q8_0 for most models — it is near-lossless and you have the memory for it. For 70B+ models, Q6_K offers excellent quality. Reserve Q4_K_M for 100B+ models or when you need maximum context length.

Gaudi 3 128GB already has strong memory bandwidth, so the next limit is often memory capacity and context headroom rather than raw decode speed. For local LLMs, fit first and bandwidth second is the right mental model.

Gaudi 3 128GB can be attractive on memory-per-dollar, but CUDA still has the broadest support across runtimes, kernels, guides, and community-tested local AI workflows. If your priority is the easiest setup and widest model compatibility, NVIDIA remains the safer choice. If your priority is value and you are comfortable with a narrower software stack, Gaudi 3 128GB can still be useful.

Gaudi 3 128GB supports up to 8× GPU scaling via PCIe. With 8× GPUs, you get 1024 GB effective memory with a 0.85× scaling factor per GPU. This enables running models like Qwen 3.5 397B A17B and Kimi K2.5 that don't fit on a single card.

Gaudi 3 128GB uses PCIe for multi-GPU communication, which has approximately 15% scaling overhead. For best multi-GPU performance, consider NVLink-equipped variants.

Usually yes. If you want to run 2-4× Gaudi 3 128GB for local AI, the bottleneck often becomes the platform, not the card. Workstation and server boards give you more CPU PCIe lanes, better x16 slot wiring, more spacing between cards, stronger power delivery, and usually more RAM capacity. Consumer x8/x8 layouts can work, but they are a common weak point in multi-GPU builds.