Phi 4 reasoning vision 15B needs ~22.2 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 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
443K
Memory
22.2 GB / 69.1 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 | 443K |
| Coding | C | Runs well | 60.9 tok/s | 3181 ms | 443K |
| Agentic Coding | C | Runs well | 60.9 tok/s | 4627 ms | 443K |
| Reasoning | C | Runs well | 60.9 tok/s | 3759 ms | 443K |
| RAG | C | Runs well | 60.9 tok/s | 5783 ms | 443K |
Inference speed
Estimated decode speed (tokens/sec) for Phi 4 reasoning vision 15B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 131.2 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 83.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 75.5 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 71.6 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 68.3 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 60.9 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 50.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 48.1 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 34.7 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 34.7 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 26.7 | Heavy offload |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 26.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 24.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 15.7 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 5.9 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C40 |
Q3_K_S | 3 | 7.4 GB | Low | C40 |
NVFP4 | 4 | 8.4 GB | Medium | C41 |
Q4_K_M | 4 | 9.2 GB | Medium | C41 |
Q5_K_M | 5 | 10.8 GB | High | C41 |
Q6_K | 6 | 12.3 GB | High | C41 |
Q8_0 | 8 | 16.1 GB | Very High | C42 |
F16Best for your GPU | 16 | 30.7 GB | Maximum | C45 |
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 startYes, Mac Studio M3 Ultra 96GB 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 22.2 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 96GB, 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 96GB receives a C grade with 60.9 tok/s and 443K context.
On Mac Studio M3 Ultra 96GB, Phi 4 reasoning vision 15B can safely use up to 443K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. Mac Studio M3 Ultra 96GB 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-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: