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

Qwen 2.5-VL 7B (VLM) on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

Qwen 2.5-VL 7B (VLM) at FP16 needs 14.0 GB for its weights and 17.8 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
17.8 GB needed / 80 GB available
Decode speed
155.5
tok/s per stream
Aggregate
1866
tok/s @ 8 concurrent
Cost / 1M tokens
$0.3721
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 GB17.8 GB155.51866268.1 ms$0.3721✓
2× H100160 GB17.8 GB264.46346149.6 ms$0.2189✓
4× H100320 GB17.8 GB528.82538389.8 ms$0.1094✓
8× H100640 GB17.8 GB105810153459.9 ms$0.0547✓
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?
4K280240.5 MB✓ fits
8K140481.0 MB✓ fits
33K351.88 GB✓ fits
66K173.76 GB✓ fits
131K87.52 GB✓ fits
Other GPUs that run Qwen 2.5-VL 7B (VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3090 24GB1×521.5$0.1332
RTX 5090 32GB1×998.4$0.1391
RTX 4090 24GB1×561.6$0.1978
AMD MI300X 192GB1×2953$0.2352
V100 SXM2 32GB1×501.4$0.2770
A100 40GB SXM41×863.6$0.2895
Frequently asked questions

Can Qwen 2.5-VL 7B (VLM) run on H100 SXM5 80GB?

Yes. Qwen 2.5-VL 7B (VLM) at FP16 needs 17.8 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-VL 7B (VLM) on H100 SXM5 80GB?

155.5 tokens/sec per stream and 1866 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 268.1 ms, and inter-token latency is 6.4 ms.

What does Qwen 2.5-VL 7B (VLM) on H100 SXM5 80GB cost per million tokens?

$0.3721 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-VL 7B (VLM) need on H100 SXM5 80GB?

14.0 GB for the weights at FP16, plus 3.8 GB of KV cache for 8 concurrent requests at 8K context — 17.8 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-VL 7B (VLM)?

It works. Compare $0.3721 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 2× RTX 3080 10GB at $0.0772, and the fastest is 1× Cerebras CS-3 (WSE-3) at 11143 tok/s.

Adjust the workload yourself

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

Open in calculator