Raises estimated decode speed by about 197%.
~$999 MSRP
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VOOZH | about |
Phi 4 reasoning vision 15B needs ~15.3 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~25 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
25.4 tok/s
TTFT
7636 ms
Safe context
87K
Memory
15.3 GB / 23.0 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 | 25.4 tok/s | 4165 ms | 87K |
| Coding | C | Runs well | 25.4 tok/s | 7636 ms | 87K |
| Agentic Coding | C | Runs well | 25.4 tok/s | 11106 ms | 87K |
| Reasoning | C | Runs well | 25.4 tok/s | 9024 ms | 87K |
| RAG | C | Runs well | 25.4 tok/s | 13883 ms | 87K |
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C46 |
Q3_K_S | 3 | 7.4 GB | Low | C47 |
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
8.4 GB |
| Medium |
| C48 |
Q4_K_M | 4 | 9.2 GB | Medium | C48 |
Q5_K_M | 5 | 10.8 GB | High | C50 |
Q6_K | 6 | 12.3 GB | High | C50 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C50 |
F16 | 16 | 30.7 GB | Maximum | F0 |