Specifications: H100 80GB vs Intel Gaudi 3
The H100 80GB and Intel Gaudi 3 represent different generations of GPU architecture for AI workloads. Memory capacity ranges from 80GB to GaudiGB, with significant differences in memory bandwidth, compute throughput (FP8/FP16/TF32), NVLink connectivity, and TDP. The Intel Gaudi 3 supports newer technologies like FP8 transformer engines, fourth-gen tensor cores, and higher-bandwidth HBM3e or HBM4 memory.
AI Inference Performance
For LLM inference, the Gaudi 3 generally achieves 30-120% higher throughput than the H100 depending on model size and batch configuration. For a 7B model at FP8 with continuous batching, the H100 serves 1,800-2,400 tok/s while the Gaudi 3 reaches 3,000-5,000 tok/s. For 70B models, tensor parallelism across 4 GPUs is needed on the H100 while the Gaudi 3 may serve the same model with 2 GPUs due to higher VRAM. Prefill latency for 4K tokens ranges from 35-55ms for the H100 versus 18-35ms for the Gaudi 3.
Training Throughput Comparison
On training workloads, the Gaudi 3 delivers 40-120% higher throughput for typical model sizes. For a 7B model at BF16 mixed precision, per-GPU throughput reaches 3,500-4,500 tok/s on the H100 versus 5,000-8,000 tok/s on the Gaudi 3. Model FLOPS utilization (MFU) ranges from 38-48% on the H100 and 42-55% on the Gaudi 3. Memory capacity constraints on the H100 require activation checkpointing for models larger than 13B, while the Gaudi 3 accommodates larger models without checkpointing.
VRAM and Model Capacity Analysis
Memory capacity is the most critical differentiator. The H100 80GB has 80GB VRAM, while the Intel Gaudi 3 has more VRAM. At FP16, a 7B model requires ~14 GB for weights, plus KV cache of ~1.5 GB per 128K context per request. INT4 quantization halves the weight memory requirement, enabling larger models or batch sizes. The VRAM gap is most impactful for long-context serving and large batch inference.
Cloud Pricing and TCO
On-demand cloud pricing for the H100 averages $2.50/hr while the Gaudi 3 averages $1.80/hr. However, cost-per-token analysis often favors the Gaudi 3 by 15-40% for sustained production workloads due to higher throughput. Reserved 12-month contracts provide 30-50% discounts. For a 64-GPU, 3-year TCO, the H100 cluster costs $533K-$949K while the Gaudi 3 cluster costs $700K-$1178K including hardware, power, cooling, and maintenance.
Which GPU Should You Choose?
Choose the H100 for: budget-constrained deployments, models under 13B that fit in available VRAM, batch inference workloads where throughput per dollar is secondary to absolute cost, and development/staging environments. Choose the Gaudi 3 for: production serving at scale, models larger than 13B parameters, workloads requiring FP8/FP4 precision, long-context inference beyond 128K tokens, and clusters above 128 GPUs where scaling efficiency matters.