Can Phi 3 Mini 3.8B run on Mac mini M4 64GB?

YES — Runs Great

B63Good

Estimated — low-sample bucket· few comparable runs

Phi 3 Mini 3.8B needs ~16.0 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 16.0 GB, 34.3 tok/s, Runs well

16.0 GB required46.1 GB available

35% VRAM used

Fit status

Runs well

Decode

34.3 tok/s

TTFT

5646 ms

Safe context

98K

Memory

16.0 GB / 46.1 GB

Memory breakdown

Weights2.3 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsPhi 3 Mini 3.8B on Mac mini M4 64GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 34.3 tok/s decode · 5.6s TTFT (warm) · 86 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	34.3 tok/s	3079 ms	98K
Coding	B	Runs well	34.3 tok/s	5646 ms	98K
Agentic Coding	B	Runs well	34.3 tok/s	8212 ms	98K
Reasoning	B	Runs well	34.3 tok/s	6672 ms	98K
RAG	B	Runs well	34.3 tok/s	10265 ms	98K

Inference speed

Phi 3 Mini 3.8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi 3 Mini 3.8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~72 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	72.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	60.8	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	60.8	Fits
RTX 4070 12GB	12 GB	Q4_K_M	60.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	53.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	53.2	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	53.2	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	53.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	53.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	53.2	Fits
RTX 4060 8GB	8 GB	Q4_K_M	38.7	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B58
Q3_K_S	3	1.9 GB	Low	B58
NVFP4	4	2.1 GB	Medium	B58
Q4_K_M	4	2.3 GB	Medium	B58
Q5_K_M	5	2.7 GB	High	B59
Q6_K	6	3.1 GB	High	B59
Q8_0	8	4.1 GB	Very High	B59
F16Best for your GPU	16	7.8 GB	Maximum	B60

Get started

Copy-paste commands to run Phi 3 Mini 3.8B on your machine.

Run

ollama run phi3:mini

升级选项

能流畅运行 Phi 3 Mini 3.8B 的硬件

MacBook Pro M4 Max 96GB经济之选

96 GB Unified (+32)546 GB/s (+426)

Raises estimated decode speed by about 55%.53.2 tok/s 解码

Raises estimated decode speed by about 55%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

MacBook Pro M2 Max 96GB最佳性价比

96 GB Unified (+32)400 GB/s (+280)

Raises estimated decode speed by about 55%.53.2 tok/s 解码

Raises estimated decode speed by about 55%.

Adds memory headroom for longer context windows and future model growth.

~$3,199 MSRP

Mac Studio M3 Ultra 96GBApple 升级

96 GB Unified (+32)819 GB/s (+699)

Raises estimated decode speed by about 55%.53.2 tok/s 解码

Raises estimated decode speed by about 55%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run Phi 3 Mini 3.8B?

Yes, Mac mini M4 64GB can run Phi 3 Mini 3.8B with a B grade (Runs well). Expected decode speed: 34.3 tok/s.

How much VRAM does Phi 3 Mini 3.8B need?

Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 16.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 3 Mini 3.8B?

The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3 Mini 3.8B run at on Mac mini M4 64GB?

On Mac mini M4 64GB, Phi 3 Mini 3.8B achieves approximately 34.3 tokens per second decode speed with a time-to-first-token of 5646ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on Mac mini M4 64GB receives a B grade with 34.3 tok/s and 98K context.

What context window can Phi 3 Mini 3.8B use on Mac mini M4 64GB?

On Mac mini M4 64GB, Phi 3 Mini 3.8B can safely use up to 98K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for Phi 3 Mini 3.8B?

Not always. Mac mini M4 64GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac mini M4 64GB See all hardware for Phi 3 Mini 3.8B

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