NVIDIA32 GB VRAM900 GB/s$0.5/hr

Qwen 2.5-VL 7B (VLM) on V100 SXM2 32GB: 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 V100 SXM2 32GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
17.8 GB needed / 32 GB available
Decode speed
41.79
tok/s per stream
Aggregate
501.4
tok/s @ 8 concurrent
Cost / 1M tokens
$0.2770
1× V100 SXM2 32GB
What changes as you add V100 SXM2 32GBs
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× V10032 GB17.8 GB41.79501.41.89 s$0.2770✓
2× V10064 GB17.8 GB71.041705961.6 ms$0.1629✓
4× V100128 GB17.8 GB142.16819495.8 ms$0.0815✓
8× V100256 GB17.8 GB284.127278262.9 ms$0.0407✓
Concurrency vs context length
On 1× V100 SXM2 32GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K76240.5 MB✓ fits
8K38481.0 MB✓ fits
33K91.88 GB✓ fits
66K43.76 GB✓ fits
131K27.52 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
A100 40GB SXM41×863.6$0.2895
AMD MI325X 288GB1×3343$0.2908
Frequently asked questions

Can Qwen 2.5-VL 7B (VLM) run on V100 SXM2 32GB?

Yes. Qwen 2.5-VL 7B (VLM) at FP16 needs 17.8 GB including the KV cache, and V100 SXM2 32GB has 32 GB — so it fits on a single GPU.

How fast is Qwen 2.5-VL 7B (VLM) on V100 SXM2 32GB?

41.79 tokens/sec per stream and 501.4 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.89 s, and inter-token latency is 23.9 ms.

What does Qwen 2.5-VL 7B (VLM) on V100 SXM2 32GB cost per million tokens?

$0.2770 per 1M output tokens at 1× V100 SXM2 32GB ($0.5000/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 V100 SXM2 32GB?

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 V100 SXM2 32GB the right choice for Qwen 2.5-VL 7B (VLM)?

It works. Compare $0.2770 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