Phi 4 reasoning vision 15B needs ~13.4 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 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
Tight fit
Decode
37.1 tok/s
TTFT
5213 ms
Safe context
40K
Memory
13.4 GB / 16.0 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 | 37.1 tok/s | 2843 ms | 40K |
| Coding | C | Tight fit | 37.1 tok/s | 5213 ms | 40K |
| Agentic Coding | C | Tight fit | 37.1 tok/s | 7582 ms | 40K |
| Reasoning | C | Tight fit | 37.1 tok/s | 6160 ms | 40K |
| RAG | C | Tight fit | 37.1 tok/s | 9477 ms | 40K |
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C50 |
Q3_K_S | 3 | 7.4 GB | Low | C51 |
NVFP4 | 4 | 8.4 GB | Medium | C51 |
Q4_K_M | 4 | 9.2 GB | Medium | C51 |
Q5_K_M | 5 | 10.8 GB | High | C51 |
Q6_KBest for your GPU | 6 | 12.3 GB | High | C50 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
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 startOpciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 42%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$899 MSRP
Sube la velocidad estimada de decodificación alrededor de un 104%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$999 MSRP
Sube la velocidad estimada de decodificación alrededor de un 135%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$11,500 MSRP
Yes, Radeon RX 7900M 16GB can run Phi 4 reasoning vision 15B with a C grade (Tight fit). Expected decode speed: 37.1 tok/s.
Phi 4 reasoning vision 15B (15B parameters) requires approximately 13.4 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 Radeon RX 7900M 16GB, Phi 4 reasoning vision 15B achieves approximately 37.1 tokens per second decode speed with a time-to-first-token of 5213ms using Q4_K_M quantization.
For coding workloads, Phi 4 reasoning vision 15B on Radeon RX 7900M 16GB receives a C grade with 37.1 tok/s and 40K context.
On Radeon RX 7900M 16GB, Phi 4 reasoning vision 15B can safely use up to 40K 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-rx-7900m-16gb" 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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