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

Qwen 3 8B on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

Qwen 3 8B at FP16 needs 16.0 GB for its weights and 25.7 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
25.7 GB needed / 80 GB available
Decode speed
136.1
tok/s per stream
Aggregate
1633
tok/s @ 8 concurrent
Cost / 1M tokens
$0.4252
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 GB25.7 GB136.11633302.1 ms$0.4252✓
2× H100160 GB25.7 GB231.45553166.7 ms$0.2501✓
4× H100320 GB25.7 GB462.72221198.4 ms$0.1251✓
8× H100640 GB25.7 GB925.48884264.2 ms$0.0625✓
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?
4K105618.5 MB✓ fits
8K521.21 GB✓ fits
33K134.83 GB✓ fits
66K69.66 GB✓ fits
131K319.33 GB✓ fits
Other GPUs that run Qwen 3 8B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB1×873.6$0.1590
AMD MI300X 192GB1×2584$0.2688
V100 SXM2 32GB1×438.8$0.3166
A100 40GB SXM41×755.6$0.3309
AMD MI325X 288GB1×2925$0.3324
A100 80GB SXM41×975.0$0.3419
Frequently asked questions

Can Qwen 3 8B run on H100 SXM5 80GB?

Yes. Qwen 3 8B at FP16 needs 25.7 GB including the KV cache, and H100 SXM5 80GB has 80 GB — so it fits on a single GPU.

How fast is Qwen 3 8B on H100 SXM5 80GB?

136.1 tokens/sec per stream and 1633 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 302.1 ms, and inter-token latency is 7.3 ms.

What does Qwen 3 8B on H100 SXM5 80GB cost per million tokens?

$0.4252 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 8B need on H100 SXM5 80GB?

16.0 GB for the weights at FP16, plus 9.7 GB of KV cache for 8 concurrent requests at 8K context — 25.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is H100 SXM5 80GB the right choice for Qwen 3 8B?

It works. Compare $0.4252 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0441, and the fastest is 1× Cerebras CS-3 (WSE-3) at 9750 tok/s.

Adjust the workload yourself

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

Open in calculator