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.
~$329 MSRP
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VOOZH | about |
Qwen 3 14B needs ~12.7 GB but RX 6650 XT 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
4.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.1 tok/s
TTFT
37698 ms
Safe context
4K
Memory
12.7 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 12.7 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 | 6.4 tok/s | 16617 ms | 4K |
| Coding | F | Too heavy | 5.1 tok/s | 37698 ms | 4K |
| Agentic Coding | F | Too heavy | 3.5 tok/s | 79433 ms | 4K |
| Reasoning | F | Too heavy | 5.1 tok/s | 44552 ms | 4K |
| RAG | F | Too heavy | 3.5 tok/s | 99291 ms | 4K |
How Qwen 3 14B (14B params) fits at each quantization level on RX 6650 XT 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 options
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.
~$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.
~$349 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 302%.
~$449 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.
~$2,000 MSRP