Apple192 GB VRAM800 GB/s$0/hr

Pixtral 12B (VLM) on Mac M2 Ultra (192GB unified): 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 Mac M2 Ultra (192GB unified). Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
34.7 GB needed / 192 GB available
Decode speed
21.67
tok/s per stream
Aggregate
260.0
tok/s @ 8 concurrent
Cost / 1M tokens
—
1× Mac M2 Ultra (192GB unified) · quote required
What changes as you add Mac M2 Ultra (192GB unified)s
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× Mac192 GB34.7 GB21.67260.014.64 s—✓
2× Mac384 GB34.7 GB36.83884.07.34 s—✓
4× Mac768 GB34.7 GB73.6735363.68 s—✓
8× Mac1536 GB34.7 GB147.3141441.86 s—✓
Concurrency vs context length
On 1× Mac M2 Ultra (192GB unified) at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K250687.2 MB✓ fits
8K1251.34 GB✓ fits
33K315.37 GB✓ fits
66K1510.74 GB✓ fits
131K721.47 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 Mac M2 Ultra (192GB unified)?

Yes. Pixtral 12B (VLM) at FP16 needs 34.7 GB including the KV cache, and Mac M2 Ultra (192GB unified) has 192 GB — so it fits on a single GPU.

How fast is Pixtral 12B (VLM) on Mac M2 Ultra (192GB unified)?

21.67 tokens/sec per stream and 260.0 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 14.64 s, and inter-token latency is 46.2 ms.

What does Pixtral 12B (VLM) on Mac M2 Ultra (192GB unified) cost per million tokens?

— per 1M output tokens at 1× Mac M2 Ultra (192GB unified). 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 Mac M2 Ultra (192GB unified)?

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 Mac M2 Ultra (192GB unified) the right choice for Pixtral 12B (VLM)?

It works. Compare — 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