Best GPUs for AI Inference

Serving a model is a different problem from training one. There is no optimizer state and no gradient traffic, so the memory requirement collapses to the weights plus the KV cache plus activations. The question becomes how much throughput you can extract per dollar per hour, and the answer is usually the smallest GPU your weights comfortably fit on rather than the largest one available.

Token generation is memory-bandwidth bound, not compute bound. Each generated token requires reading the entire set of active weights out of GPU memory, so bandwidth sets the ceiling on single-stream latency. This is why an accelerator with modest FLOPS but fast HBM can outperform a higher-FLOPS card with GDDR for decode, and why batching matters so much: larger batches amortize those weight reads across more tokens and move the workload back toward compute bound.

Quantization changes the hardware calculus more than any other single decision. Running weights at INT8 or FP8 roughly halves the memory footprint and the bandwidth per token versus BF16, often moving a model down a hardware tier with limited quality loss. INT4 halves it again with more measurable degradation. Decide the numeric format first, then size the GPU, not the other way around.

Do not forget the KV cache. At long context lengths and high concurrency it can rival or exceed the weights in size, and it grows linearly with both. A deployment sized only for weights will fit on paper and then fall over under real concurrency — size for your target context length and batch size together.