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

Pixtral 12B (VLM) on H100 SXM5 80GB: Does It Fit, How Fast, What It Costs

Pixtral 12B (VLM) at FP16 needs 24.0 GB for its weights and 34.7 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
34.7 GB needed / 80 GB available
Decode speed
90.73
tok/s per stream
Aggregate
1089
tok/s @ 8 concurrent
Cost / 1M tokens
$0.6378
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 GB34.7 GB90.731089438.2 ms$0.6378✓
2× H100160 GB34.7 GB154.23702235.1 ms$0.3752✓
4× H100320 GB34.7 GB308.514807132.5 ms$0.1876✓
8× H100640 GB34.7 GB617.05922881.3 ms$0.0938✓
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?
4K83687.2 MB✓ fits
8K411.34 GB✓ fits
33K105.37 GB✓ fits
66K510.74 GB✓ fits
131K221.47 GB✓ fits
Other GPUs that run Pixtral 12B (VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×1980$0.1403
AMD MI300X 192GB1×1723$0.4032
A100 40GB SXM41×503.8$0.4963
AMD MI325X 288GB1×1950$0.4986
A100 80GB SXM41×650.0$0.5128
Intel Gaudi 3 128GB1×1073$0.5180
Frequently asked questions

Can Pixtral 12B (VLM) run on H100 SXM5 80GB?

Yes. Pixtral 12B (VLM) at FP16 needs 34.7 GB including the KV cache, and H100 SXM5 80GB has 80 GB — so it fits on a single GPU.

How fast is Pixtral 12B (VLM) on H100 SXM5 80GB?

90.73 tokens/sec per stream and 1089 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 438.2 ms, and inter-token latency is 11.0 ms.

What does Pixtral 12B (VLM) on H100 SXM5 80GB cost per million tokens?

$0.6378 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 Pixtral 12B (VLM) need on H100 SXM5 80GB?

24.0 GB for the weights at FP16, plus 10.7 GB of KV cache for 8 concurrent requests at 8K context — 34.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is H100 SXM5 80GB the right choice for Pixtral 12B (VLM)?

It works. Compare $0.6378 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0662, and the fastest is 1× Cerebras CS-3 (WSE-3) at 6500 tok/s.

Adjust the workload yourself

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

Open in calculator