NVIDIA48 GB VRAM960 GB/s$0.8/hr

Pixtral 12B (VLM) on RTX 6000 Ada 48GB: 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 RTX 6000 Ada 48GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
34.7 GB needed / 48 GB available
Decode speed
26.00
tok/s per stream
Aggregate
312.0
tok/s @ 8 concurrent
Cost / 1M tokens
$0.7123
1× RTX 6000 Ada 48GB
What changes as you add RTX 6000 Ada 48GBs
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× RTX48 GB34.7 GB26.00312.04.36 s$0.7123✓
2× RTX96 GB34.7 GB44.2010612.20 s$0.4190✓
4× RTX192 GB34.7 GB88.4042431.11 s$0.2095✓
8× RTX384 GB34.7 GB176.816973572.2 ms$0.1047✓
Concurrency vs context length
On 1× RTX 6000 Ada 48GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K35687.2 MB✓ fits
8K171.34 GB✓ fits
33K45.37 GB✓ fits
66K210.74 GB✓ fits
131K121.47 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 RTX 6000 Ada 48GB?

Yes. Pixtral 12B (VLM) at FP16 needs 34.7 GB including the KV cache, and RTX 6000 Ada 48GB has 48 GB — so it fits on a single GPU.

How fast is Pixtral 12B (VLM) on RTX 6000 Ada 48GB?

26.00 tokens/sec per stream and 312.0 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 4.36 s, and inter-token latency is 38.5 ms.

What does Pixtral 12B (VLM) on RTX 6000 Ada 48GB cost per million tokens?

$0.7123 per 1M output tokens at 1× RTX 6000 Ada 48GB ($0.8000/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 RTX 6000 Ada 48GB?

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 RTX 6000 Ada 48GB the right choice for Pixtral 12B (VLM)?

It works. Compare $0.7123 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