Phi 4 reasoning vision 15B needs ~15.3 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~50 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
50.4 tok/s
TTFT
3844 ms
Safe context
168K
Memory
15.3 GB / 32.0 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 | 50.4 tok/s | 2097 ms | 168K |
| Coding | C | Runs well | 50.4 tok/s | 3844 ms | 168K |
| Agentic Coding | C | Runs well | 50.4 tok/s | 5592 ms | 168K |
| Reasoning | C | Runs well | 50.4 tok/s | 4543 ms | 168K |
| RAG | C | Runs well | 50.4 tok/s | 6990 ms | 168K |
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C44 |
Q3_K_S | 3 | 7.4 GB | Low | C45 |
NVFP4 | 4 | 8.4 GB | Medium | C45 |
Q4_K_M | 4 | 9.2 GB | Medium | C45 |
Q5_K_M | 5 | 10.8 GB | High | C46 |
Q6_K | 6 | 12.3 GB | High | C47 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C49 |
F16 | 16 | 30.7 GB | Maximum | F0 |
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-Optionen
Yes, RTX 5000 Ada 32GB can run Phi 4 reasoning vision 15B with a C grade (Runs well). Expected decode speed: 50.4 tok/s.
Phi 4 reasoning vision 15B (15B parameters) requires approximately 15.3 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 RTX 5000 Ada 32GB, Phi 4 reasoning vision 15B achieves approximately 50.4 tokens per second decode speed with a time-to-first-token of 3844ms using Q4_K_M quantization.
For coding workloads, Phi 4 reasoning vision 15B on RTX 5000 Ada 32GB receives a C grade with 50.4 tok/s and 168K context.
On RTX 5000 Ada 32GB, Phi 4 reasoning vision 15B can safely use up to 168K 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-rtx-5000-ada-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
