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

Qwen 2.5 72B on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

Qwen 2.5 72B at FP8 (E4M3) needs 72.7 GB for its weights and 94.2 GB in total at 8K context with 8 concurrent users. That means it needs 2× H100 SXM5 80GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 2×
94.2 GB needed / 160 GB available
Decode speed
76.38
tok/s per stream
Aggregate
1833
tok/s @ 8 concurrent
Cost / 1M tokens
$0.7577
2× 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 GB94.2 GB0.0000.000——✗
2× H100160 GB94.2 GB76.3818331.25 s$0.7577✓
4× H100320 GB94.2 GB152.87332638.1 ms$0.3788✓
8× H100640 GB94.2 GB305.529329334.1 ms$0.1894✓
Concurrency vs context length
On 2× H100 SXM5 80GB at FP8 (E4M3). KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K651.34 GB✓ fits
8K322.68 GB✓ fits
33K810.74 GB✓ fits
66K421.47 GB✓ fits
131K242.95 GB✓ fits
Other GPUs that run Qwen 2.5 72B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB4×3922$0.1416
A100 80GB SXM42×1094$0.6092
AMD MI300X 192GB1×853.0$0.8142
L40S 48GB2×472.8$0.8226
AMD MI325X 288GB1×965.6$1.01
Intel Gaudi 3 128GB1×531.1$1.05
Frequently asked questions

Can Qwen 2.5 72B run on H100 SXM5 80GB?

Yes, but it needs 2× H100 SXM5 80GB. At FP8 (E4M3) the total footprint is 94.2 GB (72.7 GB of weights plus 21.5 GB of KV cache at 8K × 8 concurrent users), against 160 GB of available VRAM across 2 GPUs.

How fast is Qwen 2.5 72B on H100 SXM5 80GB?

76.38 tokens/sec per stream and 1833 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.25 s, and inter-token latency is 13.1 ms.

What does Qwen 2.5 72B on H100 SXM5 80GB cost per million tokens?

$0.7577 per 1M output tokens at 2× 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 72B need on H100 SXM5 80GB?

72.7 GB for the weights at FP8 (E4M3), plus 21.5 GB of KV cache for 8 concurrent requests at 8K context — 94.2 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 72B?

It works. Compare $0.7577 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 4080 16GB at $0.1062, and the fastest is 4× Cerebras CS-3 (WSE-3) at 43774 tok/s.

Adjust the workload yourself

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

Open in calculator