NVIDIA80 GB VRAM3,350 GB/s$2.5/hr

Qwen 3 4B on H100 SXM5 80GB: 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 H100 SXM5 80GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
17.7 GB needed / 80 GB available
Decode speed
272.2
tok/s per stream
Aggregate
3266
tok/s @ 8 concurrent
Cost / 1M tokens
$0.2126
1× H100 SXM5 80GB
What changes as you add H100 SXM5 80GBs
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× H10080 GB17.7 GB272.23266166.1 ms$0.2126✓
2× H100160 GB17.7 GB462.71110598.4 ms$0.1251✓
4× H100320 GB17.7 GB925.44442164.2 ms$0.0625✓
8× H100640 GB17.7 GB185117768447.1 ms$0.0313✓
Concurrency vs context length
On 1× H100 SXM5 80GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K119618.5 MB✓ fits
8K591.21 GB✓ fits
33K144.83 GB✓ fits
66K79.66 GB✓ fits
131K319.33 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
V100 SXM2 32GB1×877.5$0.1583
A100 40GB SXM41×1511$0.1654
Frequently asked questions

Can Qwen 3 4B run on H100 SXM5 80GB?

Yes. Qwen 3 4B at FP16 needs 17.7 GB including the KV cache, and H100 SXM5 80GB has 80 GB — so it fits on a single GPU.

How fast is Qwen 3 4B on H100 SXM5 80GB?

272.2 tokens/sec per stream and 3266 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 166.1 ms, and inter-token latency is 3.7 ms.

What does Qwen 3 4B on H100 SXM5 80GB cost per million tokens?

$0.2126 per 1M output tokens at 1× H100 SXM5 80GB ($2.50/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 H100 SXM5 80GB?

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 H100 SXM5 80GB the right choice for Qwen 3 4B?

It works. Compare $0.2126 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