Vicuna 7B needs ~13.8 GB but RX 590 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
5.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.7 tok/s
TTFT
33996 ms
Safe context
4K
Memory
13.8 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 13.8 GB, but this setup only exposes 8.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 11.8 tok/s | 8920 ms | 4K |
| Coding | F | Too heavy | 5.7 tok/s | 33996 ms | 4K |
| Agentic Coding | F | Too heavy | 3.9 tok/s | 72822 ms | 4K |
| Reasoning | F | Too heavy | 5.7 tok/s | 40177 ms | 4K |
| RAG | F | Too heavy | 3.9 tok/s | 91027 ms | 4K |
How Vicuna 7B (7B params) fits at each quantization level on RX 590 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C54 |
Q3_K_S | 3 | 3.4 GB | Low | C54 |
NVFP4 | 4 | 3.9 GB | Medium | C54 |
Q4_K_M | 4 | 4.3 GB | Medium | C54 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C53 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Opções de upgrade
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 461%.
~$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.
~$899 MSRP
No, Vicuna 7B requires more memory than RX 590 8GB provides.
Vicuna 7B (7B parameters) requires approximately 13.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Vicuna 7B is Q4_K_M, which balances quality and memory efficiency.
On RX 590 8GB, Vicuna 7B achieves approximately 5.7 tokens per second decode speed with a time-to-first-token of 33996ms using Q4_K_M quantization.
For coding workloads, Vicuna 7B on RX 590 8GB receives a F grade with 5.7 tok/s and 4K context.
On RX 590 8GB, Vicuna 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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/vicuna-7b-on-rx-590-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
