Phi 4 reasoning vision 15B needs ~25.6 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~26 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
26.2 tok/s
TTFT
7381 ms
Safe context
622K
Memory
25.6 GB / 92.2 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 | C | Runs well | 26.2 tok/s | 4026 ms | 622K |
| Coding | C | Runs well | 26.2 tok/s | 7381 ms | 622K |
| Agentic Coding | C | Runs well | 26.2 tok/s | 10736 ms | 622K |
| Reasoning | C | Runs well | 26.2 tok/s | 8723 ms | 622K |
| RAG | C | Runs well | 26.2 tok/s | 13420 ms | 622K |
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | D39 |
Q3_K_S | 3 | 7.4 GB | Low | D39 |
NVFP4 | 4 |
Copy-paste commands to run Phi 4 reasoning vision 15B on your machine.
Run
lms load hf-jamesburton--phi-4-reasoning-vision-15b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 132%.
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Raises estimated decode speed by about 702%.
Adds memory headroom for longer context windows and future model growth.
~$8,000 MSRP
Yes, MacBook Pro M3 Max 128GB can run Phi 4 reasoning vision 15B with a C grade (Runs well). Expected decode speed: 26.2 tok/s.
Phi 4 reasoning vision 15B (15B parameters) requires approximately 25.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 4 reasoning vision 15B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 128GB, Phi 4 reasoning vision 15B achieves approximately 26.2 tokens per second decode speed with a time-to-first-token of 7381ms using Q4_K_M quantization.
For coding workloads, Phi 4 reasoning vision 15B on MacBook Pro M3 Max 128GB receives a C grade with 26.2 tok/s and 622K context.
On MacBook Pro M3 Max 128GB, Phi 4 reasoning vision 15B can safely use up to 622K tokens of context. The model's official context limit is —, 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/hf-jamesburton--phi-4-reasoning-vision-15b-gguf-on-m3-max-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
8.4 GB |
| Medium |
| D39 |
Q4_K_M | 4 | 9.2 GB | Medium | D39 |
Q5_K_M | 5 | 10.8 GB | High | D40 |
Q6_K | 6 | 12.3 GB | High | D40 |
Q8_0 | 8 | 16.1 GB | Very High | C40 |
F16Best for your GPU | 16 | 30.7 GB | Maximum | C42 |
Not always. MacBook Pro M3 Max 128GB 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.