NVIDIA80 GB VRAM3,350 GB/s$2.5/hr

Qwen 2.5 14B on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

Qwen 2.5 14B at FP16 needs 28.0 GB for its weights and 40.9 GB in total at 8K context with 8 concurrent users. That means it fits on a single H100 SXM5 80GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
40.9 GB needed / 80 GB available
Decode speed
77.77
tok/s per stream
Aggregate
933.2
tok/s @ 8 concurrent
Cost / 1M tokens
$0.7441
1× H100 SXM5 80GB
What changes as you add H100 SXM5 80GBs
Tensor-parallel degree vs VRAM, throughput, and cost. Cost scales linearly with GPU count; decode throughput scales near-linearly too, so cost per token stays roughly flat.
GPUsTotal VRAMVRAM neededDecode tok/sAggregate tok/sTTFTCost / 1M tokFits?
1× H10080 GB40.9 GB77.77933.2506.2 ms$0.7441✓
2× H100160 GB40.9 GB132.23173269.3 ms$0.4377✓
4× H100320 GB40.9 GB264.412692149.6 ms$0.2189✓
8× H100640 GB40.9 GB528.85076789.8 ms$0.1094✓
Concurrency vs context length
On 1× H100 SXM5 80GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K64824.6 MB✓ fits
8K321.61 GB✓ fits
33K86.44 GB✓ fits
66K412.88 GB✓ fits
131K225.77 GB✓ fits
Other GPUs that run Qwen 2.5 14B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×1697$0.1637
AMD MI300X 192GB1×1476$0.4704
AMD MI325X 288GB1×1671$0.5817
A100 80GB SXM41×557.1$0.5983
Intel Gaudi 3 128GB1×919.3$0.6043
B200 SXM 192GB1×2229$0.7479
Frequently asked questions

Can Qwen 2.5 14B run on H100 SXM5 80GB?

Yes. Qwen 2.5 14B at FP16 needs 40.9 GB including the KV cache, and H100 SXM5 80GB has 80 GB — so it fits on a single GPU.

How fast is Qwen 2.5 14B on H100 SXM5 80GB?

77.77 tokens/sec per stream and 933.2 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 506.2 ms, and inter-token latency is 12.9 ms.

What does Qwen 2.5 14B on H100 SXM5 80GB cost per million tokens?

$0.7441 per 1M output tokens at 1× H100 SXM5 80GB ($2.50/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Qwen 2.5 14B need on H100 SXM5 80GB?

28.0 GB for the weights at FP16, plus 12.9 GB of KV cache for 8 concurrent requests at 8K context — 40.9 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is H100 SXM5 80GB the right choice for Qwen 2.5 14B?

It works. Compare $0.7441 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0386, and the fastest is 8× RTX 3080 10GB at 11517 tok/s.

Adjust the workload yourself

Same formulas, your context length, concurrency, and quantisation.

Open in calculator