Apple192 GB VRAM800 GB/s$0/hr

Qwen 2.5-VL 7B (VLM) on Mac M2 Ultra (192GB unified): Does It Fit, How Fast, What It Costs

Qwen 2.5-VL 7B (VLM) at FP16 needs 14.0 GB for its weights and 17.8 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
17.8 GB needed / 192 GB available
Decode speed
37.14
tok/s per stream
Aggregate
445.7
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 GB17.8 GB37.14445.78.55 s—✓
2× Mac384 GB17.8 GB63.1415154.29 s—✓
4× Mac768 GB17.8 GB126.360622.16 s—✓
8× Mac1536 GB17.8 GB252.6242471.10 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?
4K757240.5 MB✓ fits
8K378481.0 MB✓ fits
33K941.88 GB✓ fits
66K473.76 GB✓ fits
131K237.52 GB✓ fits
Other GPUs that run Qwen 2.5-VL 7B (VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3090 24GB1×521.5$0.1332
RTX 5090 32GB1×998.4$0.1391
RTX 4090 24GB1×561.6$0.1978
AMD MI300X 192GB1×2953$0.2352
V100 SXM2 32GB1×501.4$0.2770
A100 40GB SXM41×863.6$0.2895
Frequently asked questions

Can Qwen 2.5-VL 7B (VLM) run on Mac M2 Ultra (192GB unified)?

Yes. Qwen 2.5-VL 7B (VLM) at FP16 needs 17.8 GB including the KV cache, and Mac M2 Ultra (192GB unified) has 192 GB — so it fits on a single GPU.

How fast is Qwen 2.5-VL 7B (VLM) on Mac M2 Ultra (192GB unified)?

37.14 tokens/sec per stream and 445.7 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 8.55 s, and inter-token latency is 26.9 ms.

What does Qwen 2.5-VL 7B (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 Qwen 2.5-VL 7B (VLM) need on Mac M2 Ultra (192GB unified)?

14.0 GB for the weights at FP16, plus 3.8 GB of KV cache for 8 concurrent requests at 8K context — 17.8 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is Mac M2 Ultra (192GB unified) the right choice for Qwen 2.5-VL 7B (VLM)?

It works. Compare — per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 2× RTX 3080 10GB at $0.0772, and the fastest is 1× Cerebras CS-3 (WSE-3) at 11143 tok/s.

Adjust the workload yourself

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

Open in calculator