Should I choose H100s over A100s for inference?

Usually yes — if your inference workload can use them efficiently and you can justify the cost.
But for many deployment setups, A100s are still the better value.

Choose H100s for inference if:

• You need maximum throughput / lowest latency
• Your model is large and benefits from the H100’s faster tensor cores and higher memory bandwidth
• You’re serving FP8 / newer precision formats
• You’re already running near the limits of A100 and want headroom
• Power efficiency per token matters at scale

Choose A100s if:

• Cost is a major concern
• Your inference stack isn’t optimized to exploit H100 features
• Your model is small/medium and not GPU-bound
• You already own A100s and they meet your latency/throughput goals
• You’re memory-limited but not compute-limited, and H100’s extra performance won’t translate into proportional savings

Rule of thumb

• If you’re building a new high-scale inference service for modern LLMs: prefer H100
• If you want the best price/performance and A100 already meets your SLOs: stick with A100

Practical decision metric

Pick the GPU that gives you the lowest cost per generated token at your target latency, not just the highest benchmark score. In some deployments, H100 is faster but not enough faster to offset the higher capex/lease cost.

If you want, I can help you decide based on:

• model size
• batch size / concurrency
• sequence lengths
• latency target
• precision (FP16/BF16/FP8)
• whether you’re serving LLMs, embeddings, or vision models