MacBook Pro M3 Pro 36GBコスパ重視
36 GB Unified (+4)150 GB/s (+30)
B
Raises estimated decode speed by about 38%.47.2 tok/sデコード
Raises estimated decode speed by about 38%.
〜$1,999 MSRP
Can Phi 3 Mini 3.8B run on MacBook Pro M4 32GB?
YES — Runs Great
B67Good
Estimated — low-sample bucket· few comparable runs
Phi 3 Mini 3.8B needs ~12.5 GB VRAM. MacBook Pro M4 32GB has 23.0 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.
Select quantization to explore
Q4_K_M (Medium quality) — 12.5 GB, 34.3 tok/s, Runs well
12.5 GB required23.0 GB available
54% VRAM used
Fit status
Runs well
Decode
34.3 tok/s
TTFT
5646 ms
Safe context
45K
Memory
12.5 GB / 23.0 GB
Memory breakdown
Weights2.3 GB
KV Cache5.9 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsPhi 3 Mini 3.8B on MacBook Pro M4 32GB
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 | 45K |
| Coding | B | Runs well | 34.3 tok/s | 5646 ms | 45K |
| Agentic Coding | A | Runs well | 34.3 tok/s | 8212 ms | 45K |
| Reasoning | B | Runs well | 34.3 tok/s | 6672 ms | 45K |
| RAG | A | Runs well | 34.3 tok/s | 10265 ms | 45K |
Quantization options
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | B61 |
Q3_K_S | 3 | 1.9 GB | Low | B61 |
NVFP4 | 4 | 2.1 GB | Medium | B61 |
Q4_K_M | 4 | 2.3 GB | Medium | B61 |
Q5_K_M | 5 | 2.7 GB | High | B62 |
Q6_K | 6 | 3.1 GB | High | B62 |
Q8_0 | 8 | 4.1 GB | Very High | B62 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | B65 |
Get started
Copy-paste commands to run Phi 3 Mini 3.8B on your machine.
Run
ollama run phi3:miniアップグレードオプション
Phi 3 Mini 3.8Bを快適に動かすハードウェア
Frequently asked questions
Can MacBook Pro M4 32GB run Phi 3 Mini 3.8B?
Yes, MacBook Pro M4 32GB 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 12.5 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 MacBook Pro M4 32GB?
On MacBook Pro M4 32GB, 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 MacBook Pro M4 32GB run Phi 3 Mini 3.8B for coding?
For coding workloads, Phi 3 Mini 3.8B on MacBook Pro M4 32GB receives a B grade with 34.3 tok/s and 45K context.
What context window can Phi 3 Mini 3.8B use on MacBook Pro M4 32GB?
On MacBook Pro M4 32GB, Phi 3 Mini 3.8B can safely use up to 45K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 32GB as fast as VRAM for Phi 3 Mini 3.8B?
Not always. MacBook Pro M4 32GB 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-3-mini-3.8b-on-m4-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: