Apple128 GB VRAM546 GB/s$0/hr

Phi-3 Mini 3.8B on Mac M4 Max (128GB): 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 M4 Max (128GB). Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
33.4 GB needed / 128 GB available
Decode speed
46.70
tok/s per stream
Aggregate
560.4
tok/s @ 8 concurrent
Cost / 1M tokens
—
1× Mac M4 Max (128GB) · quote required
What changes as you add Mac M4 Max (128GB)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× Mac128 GB33.4 GB46.70560.47.38 s—✓
2× Mac256 GB33.4 GB79.3919053.70 s—✓
4× Mac512 GB33.4 GB158.876211.87 s—✓
8× Mac1024 GB33.4 GB317.530484948.7 ms—✓
Concurrency vs context length
On 1× Mac M4 Max (128GB) at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K741.61 GB✓ fits
8K373.22 GB✓ fits
33K912.88 GB✓ fits
66K425.77 GB✓ fits
131K251.54 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 M4 Max (128GB)?

Yes. Phi-3 Mini 3.8B at FP16 needs 33.4 GB including the KV cache, and Mac M4 Max (128GB) has 128 GB — so it fits on a single GPU.

How fast is Phi-3 Mini 3.8B on Mac M4 Max (128GB)?

46.70 tokens/sec per stream and 560.4 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 7.38 s, and inter-token latency is 21.4 ms.

What does Phi-3 Mini 3.8B on Mac M4 Max (128GB) cost per million tokens?

— per 1M output tokens at 1× Mac M4 Max (128GB). 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 M4 Max (128GB)?

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 M4 Max (128GB) 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