Raises estimated decode speed by about 245%.
~$15,000 MSRP
Phi 4 reasoning vision 15B needs ~39.5 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~61 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
60.9 tok/s
TTFT
3181 ms
Safe context
1.3M
Memory
39.5 GB / 184.3 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 | C | Runs well | 60.9 tok/s | 1735 ms | 1.3M |
| Coding | C | Runs well | 60.9 tok/s | 3181 ms | 1.3M |
| Agentic Coding | C | Runs well | 60.9 tok/s | 4627 ms | 1.3M |
| Reasoning | C | Runs well | 60.9 tok/s | 3759 ms | 1.3M |
| RAG | C | Runs well | 60.9 tok/s | 5783 ms | 1.3M |
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | D37 |
Q3_K_S | 3 | 7.4 GB | Low | D37 |
NVFP4 | 4 | 8.4 GB | Medium | D37 |
Q4_K_M | 4 | 9.2 GB | Medium | D37 |
Q5_K_M | 5 | 10.8 GB | High | D37 |
Q6_K | 6 | 12.3 GB | High | D37 |
Q8_0 | 8 | 16.1 GB | Very High | D37 |
F16Best for your GPU | 16 | 30.7 GB | Maximum | D39 |
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 start升级选项
Raises estimated decode speed by about 245%.
~$15,000 MSRP
Raises estimated decode speed by about 245%.
~$35,000 MSRP
Yes, Mac Studio M3 Ultra 256GB can run Phi 4 reasoning vision 15B with a C grade (Runs well). Expected decode speed: 60.9 tok/s.
Phi 4 reasoning vision 15B (15B parameters) requires approximately 39.5 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 Mac Studio M3 Ultra 256GB, Phi 4 reasoning vision 15B achieves approximately 60.9 tokens per second decode speed with a time-to-first-token of 3181ms using Q4_K_M quantization.
For coding workloads, Phi 4 reasoning vision 15B on Mac Studio M3 Ultra 256GB receives a C grade with 60.9 tok/s and 1.3M context.
On Mac Studio M3 Ultra 256GB, Phi 4 reasoning vision 15B can safely use up to 1.3M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. Mac Studio M3 Ultra 256GB 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/hf-jamesburton--phi-4-reasoning-vision-15b-gguf-on-m3-ultra-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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