Apple192 GB VRAM800 GB/s$0/hr

Codestral 25.08 on Mac M2 Ultra (192GB): Does It Fit, How Fast, What It Costs

Codestral 25.08 at FP8 (E4M3) needs 22.0 GB for its weights and 37.0 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
37.0 GB needed / 192 GB available
Decode speed
35.45
tok/s per stream
Aggregate
306.8
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 GB37.0 GB35.45306.826.76 s—✓
2× Mac384 GB37.0 GB60.27122713.40 s—✓
4× Mac768 GB37.0 GB120.549096.71 s—✓
8× Mac1536 GB37.0 GB241.1196363.37 s—✓
Concurrency vs context length
On 1× Mac M2 Ultra (192GB) at FP8 (E4M3). KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K180962.1 MB✓ fits
8K901.88 GB✓ fits
33K227.52 GB✓ fits
66K1115.03 GB✓ fits
131K530.06 GB✓ fits
Other GPUs that run Codestral 25.08
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×3240$0.0857
AMD MI300X 192GB1×2819$0.2464
A100 40GB SXM41×824.3$0.3033
AMD MI325X 288GB1×3191$0.3047
A100 80GB SXM41×1064$0.3134
Intel Gaudi 3 128GB1×1755$0.3166
Frequently asked questions

Can Codestral 25.08 run on Mac M2 Ultra (192GB)?

Yes. Codestral 25.08 at FP8 (E4M3) needs 37.0 GB including the KV cache, and Mac M2 Ultra (192GB) has 192 GB — so it fits on a single GPU.

How fast is Codestral 25.08 on Mac M2 Ultra (192GB)?

35.45 tokens/sec per stream and 306.8 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 26.76 s, and inter-token latency is 28.2 ms.

What does Codestral 25.08 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 Codestral 25.08 need on Mac M2 Ultra (192GB)?

22.0 GB for the weights at FP8 (E4M3), plus 15.0 GB of KV cache for 8 concurrent requests at 8K context — 37.0 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is Mac M2 Ultra (192GB) the right choice for Codestral 25.08?

It works. Compare — per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0404, and the fastest is 1× Cerebras CS-3 (WSE-3) at 10636 tok/s.

Adjust the workload yourself

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

Open in calculator