Vicuna 13B needs ~26.1 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~69 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
68.5 tok/s
TTFT
2828 ms
Safe context
4K
Memory
26.1 GB / 48.0 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 | 68.5 tok/s | 1543 ms | 4K |
| Coding | A | Runs well | 68.5 tok/s | 2828 ms | 4K |
| Agentic Coding | A | Runs well | 68.5 tok/s | 4113 ms | 4K |
| Reasoning | A | Runs well | 68.5 tok/s | 3342 ms | 4K |
| RAG | A | Runs well | 68.5 tok/s | 5142 ms | 4K |
How Vicuna 13B (13B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B62 |
Q3_K_S | 3 | 6.4 GB | Low | B63 |
NVFP4 | 4 | 7.3 GB | Medium | B63 |
Q4_K_M | 4 | 7.9 GB | Medium | B63 |
Q5_K_M | 5 | 9.4 GB | High | B63 |
Q6_K | 6 | 10.7 GB | High | B64 |
Q8_0 | 8 | 13.9 GB | Very High | B65 |
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 | 82.1 tok/s | |
| Qwen 3.5 27B | 27B | S | 35.6 tok/s | |
| Qwen 3.6 27B | 27B | S | 35.7 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 69 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 84.9 tok/s |
Yes, NVIDIA A40 48GB can run Vicuna 13B with a A grade (Runs well). Expected decode speed: 68.5 tok/s.
Vicuna 13B (13B parameters) requires approximately 26.1 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 NVIDIA A40 48GB, Vicuna 13B achieves approximately 68.5 tokens per second decode speed with a time-to-first-token of 2828ms using Q4_K_M quantization.
For coding workloads, Vicuna 13B on NVIDIA A40 48GB receives a A grade with 68.5 tok/s and 4K context.
On NVIDIA A40 48GB, 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-a40-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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