NVIDIA32 GB VRAM900 GB/s$0.5/hr

Qwen2 7B on V100 SXM2 32GB: 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 V100 SXM2 32GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
19.0 GB needed / 32 GB available
Decode speed
38.39
tok/s per stream
Aggregate
460.6
tok/s @ 8 concurrent
Cost / 1M tokens
$0.3015
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 GB19.0 GB38.39460.62.05 s$0.3015✓
2× V10064 GB19.0 GB65.2615661.04 s$0.1774✓
4× V100128 GB19.0 GB130.56265537.0 ms$0.0887✓
8× V100256 GB19.0 GB261.025058283.5 ms$0.0443✓
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?
4K71240.5 MB✓ fits
8K35481.0 MB✓ fits
33K81.88 GB✓ fits
66K43.76 GB✓ fits
131K27.52 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
A100 40GB SXM41×793.3$0.3151
AMD MI325X 288GB1×3071$0.3166
Frequently asked questions

Can Qwen2 7B run on V100 SXM2 32GB?

Yes. Qwen2 7B at FP16 needs 19.0 GB including the KV cache, and V100 SXM2 32GB has 32 GB — so it fits on a single GPU.

How fast is Qwen2 7B on V100 SXM2 32GB?

38.39 tokens/sec per stream and 460.6 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 2.05 s, and inter-token latency is 26.1 ms.

What does Qwen2 7B on V100 SXM2 32GB cost per million tokens?

$0.3015 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 Qwen2 7B need on V100 SXM2 32GB?

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 V100 SXM2 32GB the right choice for Qwen2 7B?

It works. Compare $0.3015 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