Gaudi 2

The Gaudi 2 is a Gaudi 2-generation Intel GPU built on the Gaudi 2 architecture, manufactured on a TSMC 7nm process node with 100 billion transistors. Released in 2022, it delivers 432 TFLOPS of FP16 throughput and 432 TFLOPS BF16 — the two precision formats most commonly used for transformer model training and inference. The Gaudi 2 architecture represents Intel's approach to balancing compute throughput, memory bandwidth, and power efficiency for data center AI workloads. At 600W TDP, the Gaudi 2 sits in the high-power data center tier (600W), compatible with standard high-density GPU server configurations.

Memory capacity is 96GB of HBM2e with 2457 GB/s bandwidth. This determines which models can run without quantization: approximately 48B parameters at FP16 (2 bytes/param), 96B at INT8 (1 byte/param), or up to 192B parameters at INT4/GGUF quantization (0.5 bytes/param). These figures are theoretical maximums — actual capacity is reduced by KV cache, framework overhead, and activation memory, typically by 10–20% for inference and 30–40% for training. The arithmetic intensity ceiling is approximately 176 FLOP/byte (432 TFLOPS ÷ 2457 GB/s). Most autoregressive LLM inference falls well below this threshold, making the 2457 GB/s memory bandwidth the binding constraint on tokens-per-second throughput rather than raw TFLOPS.

The Gaudi 2 uses RoCE v2 / PCIe 4.0 for host connectivity. Without NVLink, VRAM cannot be pooled across multiple cards — the single-card 96GB capacity is the hard ceiling for model size without model sharding over slower PCIe. For workloads that exceed 96GB, the alternative is pipeline parallelism (splitting model layers across cards) rather than tensor parallelism, which introduces inter-card communication overhead at each layer boundary. This makes the Gaudi 2 best suited for workloads that fit within a single card's VRAM budget.

The primary workloads for the Gaudi 2 are Cost-effective LLM training, Transformer workloads, Intel ecosystem. Intel Gaudi 2 AI accelerator. 96GB HBM2e with 24 100GbE RoCE ports for scale-out. Available on AWS DL2q and Intel Tiber Cloud at competitive pricing. Key limitations to factor into your evaluation: Proprietary Habana SynapseAI SDK — steep learning curve vs CUDA/ROCm; Limited third-party framework support vs NVIDIA; Smaller developer community and fewer pre-optimized models. When comparing this GPU against alternatives at similar price points, the most important metrics are memory bandwidth (for inference throughput), VRAM capacity (for model size), and FP16/BF16 TFLOPS (for training speed). Raw TFLOPS figures can be misleading for inference — a GPU with lower TFLOPS but higher memory bandwidth will often outperform a higher-TFLOPS card on tokens-per-second for memory-bound autoregressive generation.

In the broader GPU market, the Gaudi 2 is a 4-year-old design that is increasingly being replaced by newer architectures in cloud deployments, though it remains available at competitive rental rates. Intel's oneAPI and OpenVINO software stack is still maturing for LLM workloads. CUDA-native libraries require porting, and community support for Intel GPUs in popular frameworks like vLLM and llama.cpp is limited compared to NVIDIA hardware. For cloud rental, availability varies significantly by provider — some specialize in this GPU tier while others may have limited stock. Compare on-demand and spot pricing across providers using the rental comparison table on this page, and factor in region availability if latency is a concern for your inference workload.