Apple192 GB VRAM800 GB/s$0/hr

Phi-3 Mini 3.8B on Mac M2 Ultra (192GB): Does It Fit, How Fast, What It Costs

Phi-3 Mini 3.8B at FP16 needs 7.6 GB for its weights and 33.4 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
33.4 GB needed / 192 GB available
Decode speed
68.42
tok/s per stream
Aggregate
821.1
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 GB33.4 GB68.42821.14.66 s—✓
2× Mac384 GB33.4 GB116.327922.34 s—✓
4× Mac768 GB33.4 GB232.6111661.19 s—✓
8× Mac1536 GB33.4 GB465.344665608.6 ms—✓
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?
4K1141.61 GB✓ fits
8K573.22 GB✓ fits
33K1412.88 GB✓ fits
66K725.77 GB✓ fits
131K351.54 GB✓ fits
Other GPUs that run Phi-3 Mini 3.8B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×6253$0.0444
AMD MI300X 192GB1×5439$0.1277
A100 40GB SXM41×1591$0.1572
AMD MI325X 288GB1×6158$0.1579
A100 80GB SXM41×2053$0.1624
Intel Gaudi 3 128GB1×3387$0.1640
Frequently asked questions

Can Phi-3 Mini 3.8B run on Mac M2 Ultra (192GB)?

Yes. Phi-3 Mini 3.8B at FP16 needs 33.4 GB including the KV cache, and Mac M2 Ultra (192GB) has 192 GB — so it fits on a single GPU.

How fast is Phi-3 Mini 3.8B on Mac M2 Ultra (192GB)?

68.42 tokens/sec per stream and 821.1 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 4.66 s, and inter-token latency is 14.6 ms.

What does Phi-3 Mini 3.8B 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 Phi-3 Mini 3.8B need on Mac M2 Ultra (192GB)?

7.6 GB for the weights at FP16, plus 25.8 GB of KV cache for 8 concurrent requests at 8K context — 33.4 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is Mac M2 Ultra (192GB) the right choice for Phi-3 Mini 3.8B?

It works. Compare — per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0209, and the fastest is 1× Cerebras CS-3 (WSE-3) at 20526 tok/s.

Adjust the workload yourself

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

Open in calculator