Apple192 GB VRAM800 GB/s$0/hr

Qwen2 7B on Mac M2 Ultra (192GB): Does It Fit, How Fast, What It Costs

Qwen2 7B at FP16 needs 15.2 GB for its weights and 19.0 GB in total at 8K context with 8 concurrent users. That means it fits on a single Mac M2 Ultra (192GB). Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
19.0 GB needed / 192 GB available
Decode speed
34.12
tok/s per stream
Aggregate
409.4
tok/s @ 8 concurrent
Cost / 1M tokens
—
1× Mac M2 Ultra (192GB) · quote required
What changes as you add Mac M2 Ultra (192GB)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 GB19.0 GB34.12409.49.31 s—✓
2× Mac384 GB19.0 GB58.0113924.67 s—✓
4× Mac768 GB19.0 GB116.055692.35 s—✓
8× Mac1536 GB19.0 GB232.0222741.19 s—✓
Concurrency vs context length
On 1× Mac M2 Ultra (192GB) at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K752240.5 MB✓ fits
8K376481.0 MB✓ fits
33K941.88 GB✓ fits
66K473.76 GB✓ fits
131K237.52 GB✓ fits
Other GPUs that run Qwen2 7B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 3090 24GB1×479.1$0.1450
RTX 5090 32GB1×917.2$0.1514
RTX 4090 24GB1×515.9$0.2154
AMD MI300X 192GB1×2713$0.2560
V100 SXM2 32GB1×460.6$0.3015
A100 40GB SXM41×793.3$0.3151
Frequently asked questions

Can Qwen2 7B run on Mac M2 Ultra (192GB)?

Yes. Qwen2 7B at FP16 needs 19.0 GB including the KV cache, and Mac M2 Ultra (192GB) has 192 GB — so it fits on a single GPU.

How fast is Qwen2 7B on Mac M2 Ultra (192GB)?

34.12 tokens/sec per stream and 409.4 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 9.31 s, and inter-token latency is 29.3 ms.

What does Qwen2 7B on Mac M2 Ultra (192GB) cost per million tokens?

— per 1M output tokens at 1× Mac M2 Ultra (192GB). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Qwen2 7B need on Mac M2 Ultra (192GB)?

15.2 GB for the weights at FP16, plus 3.8 GB of KV cache for 8 concurrent requests at 8K context — 19.0 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is Mac M2 Ultra (192GB) the right choice for Qwen2 7B?

It works. Compare — per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 2× RTX 3080 10GB at $0.0840, and the fastest is 1× Cerebras CS-3 (WSE-3) at 10236 tok/s.

Adjust the workload yourself

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

Open in calculator