Intel Gaudi 2 AI Accelerator

When evaluating the Intel Gaudi 2 AI Accelerator AI inference performance, the conversation starts and ends with memory throughput and compute density. The Gaudi 2 architecture utilizes 24 programmable Tensor Processor Cores (TPC) and a dedicated GEMM (General Matrix Multiply) engine.

The 96GB GPU for AI category is sparse, making the Gaudi 2’s memory configuration its most compelling feature. With 96GB of HBM2e, it provides enough headroom to load massive parameter counts without immediate resort to aggressive quantization.

• Memory Capacity: 96GB HBM2e
• Memory Bandwidth: 2450 GB/s
• Significance: In LLM inference, the "Time to First Token" and "Tokens per Second" are often memory-bandwidth bound. At 2.45 TB/s, the Gaudi 2 can shuttle weights to the compute cores fast enough to maintain high throughput even on models with long context windows.

The Gaudi 2 is optimized for lower-precision formats which are now the standard for efficient AI.

• BF16 TFLOPS: ~840
• FP8 TFLOPS: ~1,680
• TDP: 600W (OAM) / 300W-450W (PCIe)

Compared to the NVIDIA A100 (312 TFLOPS BF16), the Gaudi 2 offers significantly higher raw throughput on paper for training and inference. However, achieving this performance requires utilizing the Intel Gaudi SDK (SynapseAI), which integrates natively with PyTorch and TensorFlow.

A standout feature for those building clusters is the 24 integrated 100Gb Ethernet ports (RoCE v2). This allows for Large models via multi-card scaling without the need for expensive external InfiniBand switches, making it an efficient choice for hardware for running Large models via multi-card parameter setups.

The Intel Gaudi 2 AI Accelerator VRAM for large language models allows it to handle the most demanding open-weights models currently available. Because it features 96GB of VRAM, practitioners can move beyond the limitations of standard 24GB or 48GB consumer cards.

• Llama 3.1 70B: This model fits entirely within the 96GB VRAM at FP16/BF16 precision (requiring ~140GB) only via multi-card, but a single Gaudi 2 can run Llama 3.1 70B comfortably at INT8 or FP8 quantization. For those prioritizing accuracy, running the 70B model at 4-bit or 8-bit quantization on a single card yields impressive Intel Gaudi 2 AI Accelerator tokens per second, often exceeding 40-50 t/s depending on the optimization level.
• DeepSeek-R1 / Qwen 2.5 72B: Similar to the Llama 70B class, these models are the "sweet spot" for Gaudi 2. The high memory bandwidth ensures that even during long-context reasoning tasks, the decay in generation speed is minimized.
• Mistral / Mixtral 8x7B: The MoE (Mixture of Experts) architecture benefits heavily from the Gaudi 2’s TPC architecture. You can run Mixtral 8x7B at full BF16 precision on a single card with room to spare for a 32k+ context window.
• Llama 3 8B / Mistral 7B: These models are "compute-bound" on this hardware. You can run these at massive batch sizes, making Gaudi 2 an ideal best AI chip for local deployment of high-throughput inference servers.

While the Gaudi 2 supports FP8, which offers a 2x speedup over BF16 with negligible accuracy loss, practitioners should focus on BF16 for development and FP8 for production inference. The 96GB buffer means you rarely need to drop down to 4-bit GPTQ or AWQ unless you are attempting to fit 100B+ parameter models on a single unit.

The Intel Gaudi 2 is not a consumer "plug-and-play" gaming card; it is a specialized tool for best hardware for local AI agents 2025 and enterprise-grade development.

For developers building AI agents that require low-latency reasoning and large context windows, the Gaudi 2 is a powerhouse. Its ability to handle multiple concurrent model streams makes it suitable for agentic loops where a "planner" model and "executor" model must run simultaneously.

If your organization has data privacy requirements that forbid hitting OpenAI or Anthropic APIs, the Gaudi 2 is a premier choice for an on-premise inference server. It provides the VRAM necessary to run "GPT-4 class" open-weights models like the larger Llama or DeepSeek variants without the latency of a distributed cluster.

With 96GB of VRAM, this is a top-tier card for Parameter-Efficient Fine-Tuning (PEFT) and LoRA. You can fine-tune 70B parameter models on a single card, a task that would require 2-3 consumer GPUs (and suffer from P2P bottlenecks).

The Gaudi 2 generally outperforms the A100 in price-to-performance for transformer workloads. With 16GB more VRAM and higher BF16 TFLOPS, the Gaudi 2 is the superior choice for pure LLM training and inference. However, the A100 has a more mature software ecosystem (CUDA). If your workflow is strictly PyTorch-based, the transition to Gaudi 2 is seamless; if you rely on niche CUDA kernels, the A100 may be easier to implement.

The H100 (Hopper) is faster in raw FP8 compute and has the Transformer Engine advantage. However, the Gaudi 2 remains competitive due to its 96GB capacity. For models that are memory-capacity limited rather than compute-limited, the Gaudi 2 can actually outperform an H100 in terms of maximum model size per card.

Choose the Intel Gaudi 2 if you need a local LLM powerhouse with maximum VRAM and are looking to scale via Ethernet rather than proprietary interconnects. It is currently one of the most cost-effective ways to access nearly 100GB of high-speed HBM2e memory for enterprise AI workloads.