Vicuna 7B needs ~12.8 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q2_K quantization, expect ~65 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
3.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
38.0 tok/s
TTFT
5100 ms
Safe context
4K
Memory
14.4 GB / 11.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 93.8 tok/s | 1126 ms | 4K |
| Coding | F | Too heavy | 38.0 tok/s | 5100 ms | 4K |
| Agentic Coding | F | Too heavy | 14.6 tok/s | 19234 ms | 4K |
| Reasoning | F | Too heavy | 38.0 tok/s | 6027 ms | 4K |
| RAG | F | Too heavy | 14.6 tok/s | 24043 ms | 4K |
How Vicuna 7B (7B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C50 |
Q3_K_S | 3 | 3.4 GB | Low | C51 |
NVFP4 | 4 |
Copy-paste commands to run Vicuna 7B on your machine.
Run
ollama run vicunaUpgrade options
RTX 5060 Ti 16GBBudget pickMakes 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.
~$449 MSRP
RTX 4060 Ti 16GBBest valueMakes 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.
~$499 MSRP
RTX 2000 Ada 16GBNVIDIA upgradeMakes 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.
~$625 MSRP
Yes, RTX 2080 Ti 11GB can run Vicuna 7B at Q2_K quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 14.4 GB which exceeds available memory, but at Q2_K it needs only 12.8 GB. Expected decode speed: 64.7 tok/s.
Vicuna 7B (7B parameters) requires approximately 14.4 GB at Q4_K_M quantization. On RTX 2080 Ti 11GB, it fits at Q2_K using 12.8 GB.
The recommended quantization is Q4_K_M, but on RTX 2080 Ti 11GB the best fitting quantization is Q2_K, which uses 12.8 GB.
On RTX 2080 Ti 11GB, Vicuna 7B achieves approximately 64.7 tokens per second decode speed with a time-to-first-token of 2991ms using Q2_K quantization.
For coding workloads, Vicuna 7B on RTX 2080 Ti 11GB receives a F grade with 38.0 tok/s and 4K context.
On RTX 2080 Ti 11GB, Vicuna 7B can safely use up to 4K tokens of context at Q2_K quantization. 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-7b-on-rtx-2080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C52 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_M | 5 | 5.0 GB | High | C53 |
Q6_K | 6 | 5.7 GB | High | C53 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C52 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.