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

Qwen3.8 27B (VLM) on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

Qwen3.8 27B (VLM) at FP8 (E4M3) needs 27.8 GB for its weights and 33.3 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
33.3 GB needed / 80 GB available
Decode speed
117.6
tok/s per stream
Aggregate
1411
tok/s @ 8 concurrent
Cost / 1M tokens
$0.4922
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 GB33.3 GB117.61411958.0 ms$0.4922✓
2× H100160 GB33.3 GB199.94797494.8 ms$0.2895✓
4× H100320 GB33.3 GB399.719188262.4 ms$0.1448✓
8× H100640 GB33.3 GB799.576751146.2 ms$0.0724✓
Concurrency vs context length
On 1× H100 SXM5 80GB 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?
4K1241.07 GB✓ fits
8K752.15 GB✓ fits
33K228.59 GB✓ fits
66K1117.18 GB✓ fits
131K534.36 GB✓ fits
Other GPUs that run Qwen3.8 27B (VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×2566$0.1083
AMD MI300X 192GB1×2232$0.3111
A100 40GB SXM41×652.8$0.3830
AMD MI325X 288GB1×2527$0.3847
A100 80GB SXM41×842.3$0.3957
Intel Gaudi 3 128GB1×1390$0.3997
Frequently asked questions

Can Qwen3.8 27B (VLM) run on H100 SXM5 80GB?

Yes. Qwen3.8 27B (VLM) at FP8 (E4M3) needs 33.3 GB including the KV cache, and H100 SXM5 80GB has 80 GB — so it fits on a single GPU.

How fast is Qwen3.8 27B (VLM) on H100 SXM5 80GB?

117.6 tokens/sec per stream and 1411 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 958.0 ms, and inter-token latency is 8.5 ms.

What does Qwen3.8 27B (VLM) on H100 SXM5 80GB cost per million tokens?

$0.4922 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 Qwen3.8 27B (VLM) need on H100 SXM5 80GB?

27.8 GB for the weights at FP8 (E4M3), plus 5.5 GB of KV cache for 8 concurrent requests at 8K context — 33.3 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is H100 SXM5 80GB the right choice for Qwen3.8 27B (VLM)?

It works. Compare $0.4922 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0511, and the fastest is 1× Cerebras CS-3 (WSE-3) at 8423 tok/s.

Adjust the workload yourself

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

Open in calculator