Phi 3.5 Mini 4B needs ~11.8 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~56 tok/s.
Operating mode
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
Fit status
Runs well
Decode
56.0 tok/s
TTFT
3457 ms
Safe context
31K
Memory
11.8 GB / 17.3 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 56.0 tok/s | 1886 ms | 31K |
| Coding | A | Runs well | 56.0 tok/s | 3457 ms | 31K |
| Agentic Coding | B | Runs with offload (needs ~0.1 GB host RAM) | 56.0 tok/s | 5029 ms | 31K |
| Reasoning | A | Runs well | 56.0 tok/s | 4086 ms | 31K |
| RAG | B | Runs with offload (needs ~0.1 GB host RAM) | 56.0 tok/s | 6286 ms |
How Phi 3.5 Mini 4B (4B params) fits at each quantization level on MacBook Pro M4 Pro 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 |
Copy-paste commands to run Phi 3.5 Mini 4B on your machine.
Run
ollama run phi3.5Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 37.9 tok/s | |
 Magistral Small 2507 | 24B | A | 17.8 tok/s |
Yes, MacBook Pro M4 Pro 24GB can run Phi 3.5 Mini 4B with a A grade (Runs well). Expected decode speed: 56.0 tok/s.
Phi 3.5 Mini 4B (4B parameters) requires approximately 11.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3.5 Mini 4B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 24GB, Phi 3.5 Mini 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
For coding workloads, Phi 3.5 Mini 4B on MacBook Pro M4 Pro 24GB receives a A grade with 56.0 tok/s and 31K context.
On MacBook Pro M4 Pro 24GB, Phi 3.5 Mini 4B can safely use up to 31K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-3.5-mini-4b-on-m4-pro-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 31K |
2.2 GB |
| Medium |
| B62 |
Q4_K_M | 4 | 2.4 GB | Medium | B62 |
Q5_K_M | 5 | 2.9 GB | High | B62 |
Q6_K | 6 | 3.3 GB | High | B62 |
Q8_0 | 8 | 4.3 GB | Very High | B63 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | B67 |
| Devstral Small 2 24B Instruct | 24B | A | 17.8 tok/s |
| Qwen 3 14B | 14B | S | 23.4 tok/s |
| Qwen 3 8B | 8B | S | 42.6 tok/s |
Not always. MacBook Pro M4 Pro 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.