Phi-4 14B needs ~13.3 GB but Radeon Pro W7500 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
5.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.3 tok/s
TTFT
45178 ms
Safe context
4K
Memory
13.3 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 13.3 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 5.5 tok/s | 19064 ms | 4K |
| Coding | F | Too heavy | 4.3 tok/s | 45178 ms | 4K |
| Agentic Coding | F | Too heavy | 2.8 tok/s | 101533 ms | 4K |
| Reasoning | F | Too heavy | 4.3 tok/s | 53393 ms | 4K |
| RAG | F | Too heavy | 2.8 tok/s | 126917 ms | 4K |
How Phi-4 14B (14B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | F0 |
Q3_K_S | 3 | 6.9 GB | Low | F0 |
NVFP4 | 4 | 7.8 GB | Medium | F0 |
Q4_K_M | 4 | 8.5 GB | Medium | F0 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Upgrade-Optionen
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $329 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $349 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 330%.
ca. $449 MSRP
RTX A4500 20GBGrößter SprungMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $2,000 MSRP
No, Phi-4 14B requires more memory than Radeon Pro W7500 8GB provides.
Phi-4 14B (14B parameters) requires approximately 13.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4 14B is Q4_K_M, which balances quality and memory efficiency.
On Radeon Pro W7500 8GB, Phi-4 14B achieves approximately 4.3 tokens per second decode speed with a time-to-first-token of 45178ms using Q4_K_M quantization.
For coding workloads, Phi-4 14B on Radeon Pro W7500 8GB receives a F grade with 4.3 tok/s and 4K context.
On Radeon Pro W7500 8GB, Phi-4 14B can safely use up to 4K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-4-14b-on-radeon-pro-w7500-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
