RTX 4000 Ada 20GBBudget pick20 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.30.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Phi 4 reasoning vision 15B needs ~13.4 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~52 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
51.5 tok/s
TTFT
3759 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 | 51.5 tok/s | 2050 ms | 40K |
| Coding | C | Tight fit | 51.5 tok/s | 3759 ms | 40K |
| Agentic Coding | C | Tight fit | 51.5 tok/s | 5468 ms | 40K |
| Reasoning | C | Tight fit | 51.5 tok/s | 4442 ms | 40K |
| RAG | C | Tight fit | 51.5 tok/s | 6834 ms | 40K |
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on RTX 4090 Laptop 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 startUpgrade options
RTX 4000 Ada 20GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
RTX 3090 24GBBest valueRaises estimated decode speed by about 33%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
RTX 4090 24GBNVIDIA upgradeRaises estimated decode speed by about 66%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Yes, RTX 4090 Laptop 16GB can run Phi 4 reasoning vision 15B with a C grade (Tight fit). Expected decode speed: 51.5 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 RTX 4090 Laptop 16GB, Phi 4 reasoning vision 15B achieves approximately 51.5 tokens per second decode speed with a time-to-first-token of 3759ms using Q4_K_M quantization.
For coding workloads, Phi 4 reasoning vision 15B on RTX 4090 Laptop 16GB receives a C grade with 51.5 tok/s and 40K context.
On RTX 4090 Laptop 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-rtx-4090-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: