Vicuna 13B needs ~24.5 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~151 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
Runs well
Decode
151.4 tok/s
TTFT
1279 ms
Safe context
4K
Memory
24.5 GB / 32.0 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 | A | Runs well | 151.4 tok/s | 697 ms | 4K |
| Coding | A | Runs well | 151.4 tok/s | 1279 ms | 4K |
| Agentic Coding | B | Very compromised (needs ~1 GB host RAM) | 87.3 tok/s | 3225 ms | 4K |
| Reasoning | A | Runs well | 151.4 tok/s | 1511 ms | 4K |
| RAG | B | Very compromised (needs ~1 GB host RAM) | 87.3 tok/s | 4031 ms | 4K |
How Vicuna 13B (13B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B64 |
Q3_K_S | 3 | 6.4 GB | Low | B65 |
NVFP4 | 4 | 7.3 GB | Medium | B65 |
Q4_K_M | 4 | 7.9 GB | Medium | B65 |
Q5_K_M | 5 | 9.4 GB | High | B66 |
Q6_K | 6 | 10.7 GB | High | B67 |
Q8_0 | 8 | 13.9 GB | Very High | B68 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B69 |
Copy-paste commands to run Vicuna 13B on your machine.
Run
ollama run vicuna:13bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 181.6 tok/s | |
| Qwen 3.5 27B | 27B | S | 78.7 tok/s | |
| Qwen 3.6 27B | 27B | S | 79 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 128.2 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 187.8 tok/s |
Yes, RTX 5090 32GB can run Vicuna 13B with a A grade (Runs well). Expected decode speed: 151.4 tok/s.
Vicuna 13B (13B parameters) requires approximately 24.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Vicuna 13B is Q4_K_M, which balances quality and memory efficiency.
On RTX 5090 32GB, Vicuna 13B achieves approximately 151.4 tokens per second decode speed with a time-to-first-token of 1279ms using Q4_K_M quantization.
For coding workloads, Vicuna 13B on RTX 5090 32GB receives a A grade with 151.4 tok/s and 4K context.
On RTX 5090 32GB, Vicuna 13B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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/vicuna-13b-on-rtx-5090-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: