Gaudi 2 vs L40S

Memory capacity defines a key divide: the Gaudi 2's 96 GB HBM2e VRAM doubles the L40S's 48 GB GDDR6X, enabling larger models or batch sizes without splitting across GPUs. Bandwidth reinforces this: 2460 GB/s on Gaudi 2 triples the L40S's 864 GB/s, reducing bottlenecks in data-heavy training where large batches accelerate convergence.

Compute balance impacts training versus inference. Gaudi 2 matches 420 TFLOPS FP16 and FP32, ideal for training where FP32 accumulation demands parity. The L40S lags at 362 TFLOPS FP16 and 91 TFLOPS FP32, but its 724 TFLOPS FP8 excels in quantized inference, cutting latency for deployment.

Power draw affects density: Gaudi 2's 600W TDP limits racks compared to L40S's 350W, yet Ethernet scales clusters beyond PCIe 4.0 limits. Real-world throughput favors Gaudi 2 for FP32-heavy scientific tasks, while L40S suits FP8-optimized serving.

The Gaudi 2 excels in memory-intensive training of large language models exceeding 48 GB contexts. Its 96 GB HBM2e VRAM and 2460 GB/s bandwidth support massive batch sizes, speeding convergence on datasets like those for GPT-scale models.

Choose Gaudi 2 for Ethernet-based scale-out clusters in scientific computing, where 420 TFLOPS FP32 matches FP16 for precise simulations without precision loss.

The L40S fits inference-heavy deployments with its 724 TFLOPS FP8 performance, enabling low-latency serving of quantized models at half the VRAM of Gaudi 2.

Opt for L40S in power-constrained environments or PCIe setups, as its 350W TDP allows denser racks and $0.40 per hour entry pricing across 18 offers suits prototyping or Stable Diffusion pipelines.