Can Phi 3.5 Mini 4B run on Mac mini M2 24GB?
YES — Runs Great
B68Good○Estimated from fit model
Phi 3.5 Mini 4B needs ~11.8 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~27 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) — 11.8 GB, 26.6 tok/s, Runs well
11.8 GB required17.3 GB available
Memory breakdown
Weights2.4 GB
KV Cache5.9 GB
Runtime0.9 GB
Headroom2.6 GB
See how fast it feels
See how fast it feelsPhi 3.5 Mini 4B on Mac mini M2 24GB
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: 26.6 tok/s decode · 7.3s TTFT (warm) · 67 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 | 26.6 tok/s | 3964 ms | 31K |
| Coding | B | Runs well | 26.6 tok/s | 7267 ms | 31K |
| Agentic Coding | B | Runs with offload | 25.4 tok/s | 11075 ms | 31K |
| Reasoning | B | Runs well | 26.6 tok/s | 8589 ms | 31K |
| RAG | B | Runs with offload | 25.4 tok/s | 13843 ms | 31K |
Quantization options
How Phi 3.5 Mini 4B (4B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|
Q2_K | 2 | 1.6 GB | Low | B61 |
Q3_K_S | 3 | 2.0 GB | Low | B61 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Phi 3.5 Mini 4B on your machine.
Frequently asked questions
F16Best for your GPU | 16 | 8.2 GB | Maximum | B67 |
Not always. Mac mini M2 24GB 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.
