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

Qwen2 7B on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

Qwen2 7B at FP16 needs 15.2 GB for its weights and 19.0 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
19.0 GB needed / 80 GB available
Decode speed
142.9
tok/s per stream
Aggregate
1715
tok/s @ 8 concurrent
Cost / 1M tokens
$0.4050
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 GB19.0 GB142.91715289.2 ms$0.4050✓
2× H100160 GB19.0 GB242.95830160.2 ms$0.2383✓
4× H100320 GB19.0 GB485.82331895.1 ms$0.1191✓
8× H100640 GB19.0 GB971.69327262.6 ms$0.0596✓
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?
4K275240.5 MB✓ fits
8K137481.0 MB✓ fits
33K341.88 GB✓ fits
66K173.76 GB✓ fits
131K87.52 GB✓ fits
Other GPUs that run Qwen2 7B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3090 24GB1×479.1$0.1450
RTX 5090 32GB1×917.2$0.1514
RTX 4090 24GB1×515.9$0.2154
AMD MI300X 192GB1×2713$0.2560
V100 SXM2 32GB1×460.6$0.3015
A100 40GB SXM41×793.3$0.3151
Frequently asked questions

Can Qwen2 7B run on H100 SXM5 80GB?

Yes. Qwen2 7B at FP16 needs 19.0 GB including the KV cache, and H100 SXM5 80GB has 80 GB — so it fits on a single GPU.

How fast is Qwen2 7B on H100 SXM5 80GB?

142.9 tokens/sec per stream and 1715 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 289.2 ms, and inter-token latency is 7.0 ms.

What does Qwen2 7B on H100 SXM5 80GB cost per million tokens?

$0.4050 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 Qwen2 7B need on H100 SXM5 80GB?

15.2 GB for the weights at FP16, plus 3.8 GB of KV cache for 8 concurrent requests at 8K context — 19.0 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is H100 SXM5 80GB the right choice for Qwen2 7B?

It works. Compare $0.4050 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 2× RTX 3080 10GB at $0.0840, and the fastest is 1× Cerebras CS-3 (WSE-3) at 10236 tok/s.

Adjust the workload yourself

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

Open in calculator