For inference workloads, raw training performance matters less than tokens-per-second-per-dollar. An H100 is the fastest GPU available, but at $2.50–$4.00/hr, you might be able to run two L40S GPUs for the same price and serve 2× the throughput. This guide breaks down the best GPUs for AI inference by efficiency, not raw speed.
Key Metrics for Inference
For serving LLMs, three metrics dominate: VRAM capacity (limits max model size), memory bandwidth (determines token generation speed — the bottleneck in auto-regressive decoding), and FP16 compute (determines prefill/prompt processing speed). The H100 wins on all three, but the question is whether the premium is worth it for your throughput requirements.
GPU Comparison for Inference
| GPU | VRAM | Memory BW | Typical Cloud Price | Relative Tokens/$ (7B model) |
|---|---|---|---|---|
| RTX 4090 | 24 GB | 1.0 TB/s | $0.39–0.89/hr | ★★★★★ |
| NVIDIA L4 | 24 GB | 0.3 TB/s | $0.44–0.80/hr | ★★★☆☆ |
| NVIDIA L40S | 48 GB | 0.9 TB/s | $1.49–2.49/hr | ★★★★☆ |
| A100 80GB PCIe | 80 GB | 1.9 TB/s | $1.49–2.20/hr | ★★★☆☆ |
| A100 80GB SXM4 | 80 GB | 2.0 TB/s | $1.89–2.20/hr | ★★★☆☆ |
| H100 80GB SXM5 | 80 GB | 3.35 TB/s | $2.49–4.00/hr | ★★★★☆ |
The L40S: Best Value for Inference
The L40S has become the go-to inference GPU for mid-size models in 2025. With 48 GB GDDR6 memory and Ada Lovelace architecture, it serves 7B–34B models efficiently. At $1.49–1.89/hr, you get significantly better tokens-per-dollar than an A100. The L40S also supports FP8 inference via TensorRT-LLM, which nearly doubles throughput for models that can use it.
Two L40S GPUs cost roughly the same as one H100 (or less) while offering 96 GB total VRAM — enough for a 70B model in INT4 across both cards. For high-concurrency serving, horizontal scaling with L40S often beats a single H100.
RTX 4090: Best for Small Models and Prototyping
For 7B–13B model inference at low to moderate concurrency, the RTX 4090 is remarkably efficient. At $0.39–0.89/hr on Vast.ai or RunPod marketplace, you get 24 GB VRAM and 1.0 TB/s bandwidth — enough for LLaMA 3 8B at full FP16 quality. For a personal inference API or development environment, RTX 4090 instances often cost 70–80% less per token than A100s.
When the H100 Makes Sense for Inference
The H100 dominates at very high concurrency — when you're serving hundreds of simultaneous requests, the H100's compute throughput during the prefill phase (processing user prompts) becomes the bottleneck. At 1,979 TFLOPS (FP16 with sparsity) vs. L40S's 733 TFLOPS, the H100 handles prompt batches 2–3× faster. For production APIs with peak QPS requirements, H100 clusters pay for themselves.
Recommended Setup by Scale
- →Prototyping / <10 req/min: Single RTX 4090 on Vast.ai (~$0.45/hr). Run 7B–13B in INT8.
- →Small production / 10–100 req/min: Single L40S on RunPod or Lambda Labs (~$1.79/hr). Handles 34B in INT8 or 70B in INT4.
- →Medium production / 100–500 req/min: 2× L40S or 1× H100 SXM5 (~$3.00–3.50/hr). Both viable — H100 wins on latency.
- →Large production / 500+ req/min: H100 cluster (2–8 GPUs). SXM5 with NVLink for tensor-parallel serving.