H100 vs H200 vs B200 (2026): specs, price per hour, which to rent

Specs that matter, side by side

Public NVIDIA figures, dense (non-sparsity) numbers.

Rental economics today

Specs age; the ratio of spec to price is the actual decision. Live from the sheet (snapshot 2026-09-03):

Read the last two columns as "price of bandwidth" and "price of compute". The H100 usually wins compute per dollar; the H200 wins bandwidth per dollar; the B200 buys time.

Pick the H100 when…

• your model + optimizer fits in 80 GB per shard (most ≤34B full fine-tunes, all LoRA work);
• throughput per dollar is the metric — FP8 on Hopper remains the efficiency sweet spot;
• you want depth of supply: H100 pools are the deepest of the three, so interruptible slots are nearly always available.

Pick the H200 when…

• context length is the product: KV-cache at 128k eats 80 GB cards alive, 141 GB breathes;
• the workload is memory-bound (MoE inference, stencil HPC, giant batch serving) — the 43% bandwidth jump shows up almost 1:1;
• you can trade a $1.471/hr premium for fewer, fatter shards (less inter-GPU traffic, simpler parallelism).

Pick the B200 when…

• the deadline is the budget: ~2.3× H100 FP16 throughput means Monday's checkpoint on Friday;
• FP4/FP8 inference at scale — Blackwell's low-precision path is where its $/token leadership lives;
• the model shard needs 192 GB — some 100B+ configurations simply do not fit anything else per card.

The A100 dark horse

Four years old and still the best $/GB of HBM on the sheet: $0.583/hr for 80 GB. For ≤13B training runs, LoRA farms and FP64 simulation it routinely beats the newer cards on total job cost — the software stack is bulletproof and interruptible A100 supply is deep. Run the break-even math before assuming newer is cheaper.