Phi 3 Mini 3.8B needs ~11.7 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~34 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
34.3 tok/s
TTFT
5646 ms
Safe context
31K
Memory
11.7 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 | 34.3 tok/s | 3079 ms | 31K |
| Coding | A | Runs well | 34.3 tok/s | 5646 ms | 31K |
| Agentic Coding | B | Runs with offload (needs ~0 GB host RAM) | 33.2 tok/s | 8494 ms | 31K |
| Reasoning | A | Runs well | 34.3 tok/s | 6672 ms | 31K |
| RAG | B | Runs with offload (needs ~0 GB host RAM) | 33.2 tok/s | 10618 ms | 31K |
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | B62 |
Q3_K_S | 3 | 1.9 GB | Low | B63 |
NVFP4 | 4 | 2.1 GB | Medium | B63 |
Q4_K_M | 4 | 2.3 GB | Medium | B63 |
Q5_K_M | 5 | 2.7 GB | High | B63 |
Q6_K | 6 | 3.1 GB | High | B64 |
Q8_0 | 8 | 4.1 GB | Very High | B64 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | B68 |
Copy-paste commands to run Phi 3 Mini 3.8B on your machine.
Run
ollama run phi3:miniYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 15.6 tok/s | |
 Magistral Small 2507 | 24B | A | 7.3 tok/s | |
| Devstral Small 2 24B Instruct | 24B | A | 7.3 tok/s | |
| Qwen 3 14B | 14B | S | 9.6 tok/s | |
| Qwen 3.5 4B | 4B | S | 35 tok/s |
Yes, MacBook Air M4 24GB can run Phi 3 Mini 3.8B with a A grade (Runs well). Expected decode speed: 34.3 tok/s.
Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 11.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M4 24GB, 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.
For coding workloads, Phi 3 Mini 3.8B on MacBook Air M4 24GB receives a A grade with 34.3 tok/s and 31K context.
On MacBook Air M4 24GB, Phi 3 Mini 3.8B can safely use up to 31K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Not always. MacBook Air M4 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.
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-air-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: