Apple192 GB VRAM800 GB/s$0/hr

Phi-3 Mini 3.8B on Mac M2 Ultra (192GB unified): 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 unified). 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 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 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 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?
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 unified)?

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

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

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

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 unified) 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