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

Pixtral 12B (VLM) on A100 80GB SXM4: 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 A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
34.7 GB needed / 80 GB available
Decode speed
54.17
tok/s per stream
Aggregate
650.0
tok/s @ 8 concurrent
Cost / 1M tokens
$0.5128
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 GB34.7 GB54.17650.01.31 s$0.5128✓
2× A100160 GB34.7 GB92.082210671.0 ms$0.3017✓
4× A100320 GB34.7 GB184.28840350.5 ms$0.1508✓
8× A100640 GB34.7 GB368.335360190.3 ms$0.0754✓
Concurrency vs context length
On 1× A100 80GB SXM4 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
Intel Gaudi 3 128GB1×1073$0.5180
H100 SXM5 80GB1×1089$0.6378
Frequently asked questions

Can Pixtral 12B (VLM) run on A100 80GB SXM4?

Yes. Pixtral 12B (VLM) at FP16 needs 34.7 GB including the KV cache, and A100 80GB SXM4 has 80 GB — so it fits on a single GPU.

How fast is Pixtral 12B (VLM) on A100 80GB SXM4?

54.17 tokens/sec per stream and 650.0 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.31 s, and inter-token latency is 18.5 ms.

What does Pixtral 12B (VLM) on A100 80GB SXM4 cost per million tokens?

$0.5128 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 Pixtral 12B (VLM) need on A100 80GB SXM4?

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 A100 80GB SXM4 the right choice for Pixtral 12B (VLM)?

It works. Compare $0.5128 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