Phi 4 reasoning vision 15B needs ~13.7 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~24 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
23.9 tok/s
TTFT
8093 ms
Safe context
37K
Memory
13.7 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 | 23.9 tok/s | 4414 ms | 37K |
| Coding | C | Tight fit | 23.9 tok/s | 8093 ms | 37K |
| Agentic Coding | C | Runs with offload | 23.9 tok/s | 11772 ms | 37K |
| Reasoning | C | Tight fit | 23.9 tok/s | 9565 ms | 37K |
| RAG | C | Runs with offload | 23.9 tok/s | 14715 ms | 37K |
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on RTX 2000 Ada 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
RTX 4000 Ada 20GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 28%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,250 MSRP
RTX 3090 24GBMejor relación calidad-precioSube la velocidad estimada de decodificación alrededor de un 200%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,499 MSRP
RTX 4090 24GBMejora NVIDIASube la velocidad estimada de decodificación alrededor de un 250%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,599 MSRP
Yes, RTX 2000 Ada 16GB can run Phi 4 reasoning vision 15B with a C grade (Tight fit). Expected decode speed: 23.9 tok/s.
Phi 4 reasoning vision 15B (15B parameters) requires approximately 13.7 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 2000 Ada 16GB, Phi 4 reasoning vision 15B achieves approximately 23.9 tokens per second decode speed with a time-to-first-token of 8093ms using Q4_K_M quantization.
For coding workloads, Phi 4 reasoning vision 15B on RTX 2000 Ada 16GB receives a C grade with 23.9 tok/s and 37K context.
On RTX 2000 Ada 16GB, Phi 4 reasoning vision 15B can safely use up to 37K 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.
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