RTX 5090 32GBBudget pick1792 GB/s (+1382)
C
Raises estimated decode speed by about 319%.124 tok/s decode
Raises estimated decode speed by about 319%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Phi 4 reasoning vision 15B needs ~15.7 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~30 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
29.6 tok/s
TTFT
6531 ms
Safe context
109K
Memory
15.7 GB / 25.9 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 | 29.6 tok/s | 3563 ms | 109K |
| Coding | C | Runs well | 29.6 tok/s | 6531 ms | 109K |
| Agentic Coding | C | Runs well | 29.6 tok/s | 9500 ms | 109K |
| Reasoning | C | Runs well | 29.6 tok/s | 7719 ms | 109K |
| RAG | C | Runs well | 29.6 tok/s | 11875 ms | 109K |
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C45 |
Q3_K_S | 3 | 7.4 GB | Low | C46 |
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
RTX 5090 32GBBudget pickRaises estimated decode speed by about 319%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueRaises estimated decode speed by about 178%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M4 Max 36GB can run Phi 4 reasoning vision 15B with a C grade (Runs well). Expected decode speed: 29.6 tok/s.
Phi 4 reasoning vision 15B (15B parameters) requires approximately 15.7 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 M4 Max 36GB, Phi 4 reasoning vision 15B achieves approximately 29.6 tokens per second decode speed with a time-to-first-token of 6531ms using Q4_K_M quantization.
For coding workloads, Phi 4 reasoning vision 15B on MacBook Pro M4 Max 36GB receives a C grade with 29.6 tok/s and 109K context.
On MacBook Pro M4 Max 36GB, Phi 4 reasoning vision 15B can safely use up to 109K 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-m4-max-36gb" 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 |
| C47 |
Q4_K_M | 4 | 9.2 GB | Medium | C47 |
Q5_K_M | 5 | 10.8 GB | High | C48 |
Q6_K | 6 | 12.3 GB | High | C49 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C50 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Not always. MacBook Pro M4 Max 36GB 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.