Inference is the workload most GPU buyers underestimate, and the GPU for inference you pick has to earn its keep continuously rather than in bursts. The H100 remains the default choice for LLM inference in 2026, not because it's the newest card but because the software stack is mature around it. vLLM, SGLang and TensorRT-LLM all treat H100 as the baseline target, and hardware-native FP8 support (H100 was the first GPU to ship it) gives roughly double the throughput of BF16 for free once you enable it. On Llama 3.1 8B, SGLang reaches roughly 16,200 tokens per second on a single H100, about 29% ahead of vLLM's ~12,500. For a 70B model at FP8, the engine choice matters more than the hardware: TensorRT-LLM wins on raw throughput if you can absorb a compile step, vLLM is the safer default, SGLang's RadixAttention wins when requests share prefixes.
Inference splits into two phases with different bottlenecks: prefill, which processes the prompt and is compute-bound, and decode, which generates tokens one at a time and is almost entirely memory-bandwidth bound. H100's 3,350 GB/s of bandwidth (SXM5) is why it still holds up against newer cards on the decode-bound half of the job that dominates real serving traffic. Against the previous generation, H100 runs roughly 1.7x faster than A100 on Llama-class models. Against H200, the newer card cuts time-to-first-token by around 40% thanks to higher bandwidth, which matters more for latency-sensitive chat than for batch throughput. 80GB of HBM3 fits most 70B-class models at FP8 with meaningful KV cache headroom, and 7 isolated MIG instances let you split one card across multiple smaller serving jobs without buying separate hardware.
H100 inference is placement-sensitive: prompts and responses are often personal data, so jurisdiction matters as much as latency. See H100 availability by country below.
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