Vicuna 13B needs ~23.7 GB VRAM. RTX 3090 Ti 24GB has 24.0 GB. With Q4_K_M quantization, expect ~90 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 with offload
Decode
90.2 tok/s
TTFT
2145 ms
Safe context
4K
Memory
23.7 GB / 24.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 90.2 tok/s | 1170 ms | 4K |
| Coding | A | Runs with offload | 90.2 tok/s | 2145 ms | 4K |
| Agentic Coding | F | Too heavy | 28.9 tok/s | 9736 ms | 4K |
| Reasoning | A | Runs with offload | 90.2 tok/s | 2535 ms | 4K |
| RAG | F | Too heavy | 28.9 tok/s | 12170 ms | 4K |
How Vicuna 13B (13B params) fits at each quantization level on RTX 3090 Ti 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B66 |
Q3_K_S | 3 | 6.4 GB | Low | B67 |
NVFP4 | 4 | 7.3 GB | Medium | B67 |
Q4_K_M | 4 | 7.9 GB | Medium | B68 |
Q5_K_M | 5 | 9.4 GB | High | B69 |
Q6_K | 6 | 10.7 GB | High | B70 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | A71 |
F16 | 16 | 26.7 GB | Maximum | F0 |
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 | 108.2 tok/s | |
| Qwen 3.5 27B | 27B | S | 46.9 tok/s | |
| Qwen 3.6 27B | 27B | S | 47.1 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 111.9 tok/s | |
| Qwen 3.5 35B A3B | 35B | A | 60.6 tok/s |
Yes, RTX 3090 Ti 24GB can run Vicuna 13B with a A grade (Runs with offload). Expected decode speed: 90.2 tok/s.
Vicuna 13B (13B parameters) requires approximately 23.7 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 3090 Ti 24GB, Vicuna 13B achieves approximately 90.2 tokens per second decode speed with a time-to-first-token of 2145ms using Q4_K_M quantization.
For coding workloads, Vicuna 13B on RTX 3090 Ti 24GB receives a A grade with 90.2 tok/s and 4K context.
On RTX 3090 Ti 24GB, 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.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/vicuna-13b-on-rtx-3090-ti-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: