Phi 4 reasoning vision 15B needs ~20.1 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~187 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
187.2 tok/s
TTFT
1034 ms
Safe context
561K
Memory
20.1 GB / 80.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 187.2 tok/s | 564 ms | 561K |
| Coding | C | Runs well | 187.2 tok/s | 1034 ms | 561K |
| Agentic Coding | C | Runs well | 187.2 tok/s | 1504 ms | 561K |
| Reasoning | C | Runs well | 187.2 tok/s | 1222 ms | 561K |
| RAG | C | Runs well | 187.2 tok/s | 1880 ms | 561K |
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 NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | D40 |
Q3_K_S | 3 | 7.4 GB | Low | D40 |
NVFP4 | 4 | 8.4 GB | Medium | D40 |
Q4_K_M | 4 | 9.2 GB | Medium | D40 |
Q5_K_M | 5 | 10.8 GB | High | C40 |
Q6_K | 6 | 12.3 GB | High | C40 |
Q8_0 | 8 | 16.1 GB | Very High | C41 |
F16Best for your GPU | 16 | 30.7 GB | Maximum | C44 |
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, NVIDIA A100 80GB can run Phi 4 reasoning vision 15B with a C grade (Runs well). Expected decode speed: 187.2 tok/s.
Phi 4 reasoning vision 15B (15B parameters) requires approximately 20.1 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 NVIDIA A100 80GB, Phi 4 reasoning vision 15B achieves approximately 187.2 tokens per second decode speed with a time-to-first-token of 1034ms using Q4_K_M quantization.
For coding workloads, Phi 4 reasoning vision 15B on NVIDIA A100 80GB receives a C grade with 187.2 tok/s and 561K context.
On NVIDIA A100 80GB, Phi 4 reasoning vision 15B can safely use up to 561K 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-a100-80gb" 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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