NVIDIA40 GB VRAM1,550 GB/s$0.9/hr

Qwen2 7B on A100 40GB SXM4: 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 A100 40GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
19.0 GB needed / 40 GB available
Decode speed
66.11
tok/s per stream
Aggregate
793.3
tok/s @ 8 concurrent
Cost / 1M tokens
$0.3151
1× A100 40GB SXM4
What changes as you add A100 40GB SXM4s
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× A10040 GB19.0 GB66.11793.3845.4 ms$0.3151✓
2× A10080 GB19.0 GB112.42697439.0 ms$0.1854✓
4× A100160 GB19.0 GB224.810789234.5 ms$0.0927✓
8× A100320 GB19.0 GB449.543156132.3 ms$0.0463✓
Concurrency vs context length
On 1× A100 40GB SXM4 at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K105240.5 MB✓ fits
8K52481.0 MB✓ fits
33K131.88 GB✓ fits
66K63.76 GB✓ fits
131K37.52 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
AMD MI325X 288GB1×3071$0.3166
Frequently asked questions

Can Qwen2 7B run on A100 40GB SXM4?

Yes. Qwen2 7B at FP16 needs 19.0 GB including the KV cache, and A100 40GB SXM4 has 40 GB — so it fits on a single GPU.

How fast is Qwen2 7B on A100 40GB SXM4?

66.11 tokens/sec per stream and 793.3 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 845.4 ms, and inter-token latency is 15.1 ms.

What does Qwen2 7B on A100 40GB SXM4 cost per million tokens?

$0.3151 per 1M output tokens at 1× A100 40GB SXM4 ($0.9000/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 A100 40GB SXM4?

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 A100 40GB SXM4 the right choice for Qwen2 7B?

It works. Compare $0.3151 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