NVIDIA80 GB VRAM2,000 GB/s$1.2/hr

Qwen 3 32B on A100 80GB SXM4: Does It Fit, How Fast, What It Costs

Qwen 3 32B at FP8 (E4M3) needs 32.0 GB for its weights and 49.2 GB in total at 8K context with 8 concurrent users. That means it fits on a single A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
49.2 GB needed / 80 GB available
Decode speed
60.94
tok/s per stream
Aggregate
731.3
tok/s @ 8 concurrent
Cost / 1M tokens
$0.4558
1× A100 80GB SXM4
What changes as you add A100 80GB 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× A10080 GB49.2 GB60.94731.33.41 s$0.4558✓
2× A100160 GB49.2 GB103.624861.72 s$0.2681✓
4× A100320 GB49.2 GB207.29945875.0 ms$0.1341✓
8× A100640 GB49.2 GB414.439780452.5 ms$0.0670✓
Concurrency vs context length
On 1× A100 80GB SXM4 at FP8 (E4M3). KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K441.07 GB✓ fits
8K222.15 GB✓ fits
33K58.59 GB✓ fits
66K217.18 GB✓ fits
131K134.36 GB✓ fits
Other GPUs that run Qwen 3 32B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×2228$0.1247
AMD MI300X 192GB1×1938$0.3584
L40S 48GB2×1074$0.3621
AMD MI325X 288GB1×2194$0.4432
Intel Gaudi 3 128GB1×1207$0.4604
H100 SXM5 80GB1×1225$0.5670
Frequently asked questions

Can Qwen 3 32B run on A100 80GB SXM4?

Yes. Qwen 3 32B at FP8 (E4M3) needs 49.2 GB including the KV cache, and A100 80GB SXM4 has 80 GB — so it fits on a single GPU.

How fast is Qwen 3 32B on A100 80GB SXM4?

60.94 tokens/sec per stream and 731.3 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 3.41 s, and inter-token latency is 16.4 ms.

What does Qwen 3 32B on A100 80GB SXM4 cost per million tokens?

$0.4558 per 1M output tokens at 1× A100 80GB SXM4 ($1.20/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Qwen 3 32B need on A100 80GB SXM4?

32.0 GB for the weights at FP8 (E4M3), plus 17.2 GB of KV cache for 8 concurrent requests at 8K context — 49.2 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is A100 80GB SXM4 the right choice for Qwen 3 32B?

It works. Compare $0.4558 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.0294, and the fastest is 2× Cerebras CS-3 (WSE-3) at 24863 tok/s.

Adjust the workload yourself

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

Open in calculator