What should I use for inference: A10, L4, or H100?

It depends on your model size, latency target, throughput needs, and budget — but as a quick rule:

• L4: best for cost-efficient inference for small to medium models, especially when you want strong perf/$.
• A10: good for general-purpose inference and often a solid middle ground if you need a bit more memory/bandwidth than L4 or have existing A10 availability.
• H100: best for highest-performance inference on large models or very high throughput/low latency needs, but it’s usually overkill and much more expensive unless you’re serving demanding workloads.

Simple recommendation

• Small/medium models, cost-sensitive serving → L4
• Legacy/general inference, mixed workloads, decent budget → A10
• Large LLMs, high QPS, tight latency, max throughput → H100

Practical notes

• If you’re doing LLM inference, memory matters a lot:
  • L4 is typically best for smaller quantized models or lighter serving.
  • A10 can be a safer choice if you need more room for larger batches/context.
  • H100 is ideal for larger models, larger batch sizes, and production-scale throughput.
• If your workload is vision, embedding, or classical ML, L4 is often enough.
• If you need multi-GPU scaling, H100 tends to win by a lot.

If you tell me:

1. model type/size,
2. batch size or QPS,
3. latency target, and
4. whether you care more about cost or speed,

I can recommend the best one more precisely.