NVIDIA32 GB VRAM900 GB/s$0.5/hr

Qwen 3 4B on V100 SXM2 32GB: Does It Fit, How Fast, What It Costs

Qwen 3 4B at FP16 needs 8.0 GB for its weights and 17.7 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
17.7 GB needed / 32 GB available
Decode speed
73.13
tok/s per stream
Aggregate
877.5
tok/s @ 8 concurrent
Cost / 1M tokens
$0.1583
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 GB17.7 GB73.13877.51.09 s$0.1583✓
2× V10064 GB17.7 GB124.32984562.3 ms$0.0931✓
4× V100128 GB17.7 GB248.611934296.2 ms$0.0466✓
8× V100256 GB17.7 GB497.347736163.1 ms$0.0233✓
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?
4K39618.5 MB✓ fits
8K191.21 GB✓ fits
33K44.83 GB✓ fits
66K29.66 GB✓ fits
131K119.33 GB✓ fits
Other GPUs that run Qwen 3 4B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3090 24GB1×912.6$0.0761
RTX 5090 32GB1×1747$0.0795
RTX 4090 24GB1×982.8$0.1131
AMD MI300X 192GB1×5168$0.1344
A100 40GB SXM41×1511$0.1654
AMD MI325X 288GB1×5850$0.1662
Frequently asked questions

Can Qwen 3 4B run on V100 SXM2 32GB?

Yes. Qwen 3 4B at FP16 needs 17.7 GB including the KV cache, and V100 SXM2 32GB has 32 GB — so it fits on a single GPU.

How fast is Qwen 3 4B on V100 SXM2 32GB?

73.13 tokens/sec per stream and 877.5 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.09 s, and inter-token latency is 13.7 ms.

What does Qwen 3 4B on V100 SXM2 32GB cost per million tokens?

$0.1583 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 3 4B need on V100 SXM2 32GB?

8.0 GB for the weights at FP16, plus 9.7 GB of KV cache for 8 concurrent requests at 8K context — 17.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is V100 SXM2 32GB the right choice for Qwen 3 4B?

It works. Compare $0.1583 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 2× RTX 3080 10GB at $0.0441, and the fastest is 1× Cerebras CS-3 (WSE-3) at 19500 tok/s.

Adjust the workload yourself

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

Open in calculator