LMSYS
Vicuna 7B (7B parameters) requires approximately 13.9 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 16 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run Vicuna 7B on your machine.
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ollama run vicunaQuick specs
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No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | — |
Q3_K_S | 3 | 3.4 GB | Low | — |
NVFP4 | 4 | 3.9 GB | Medium | — |
Q4_K_M | 4 | 4.3 GB | Medium | — |
Q5_K_M | 5 | 5.0 GB | High | — |
Q6_K | 6 | 5.7 GB | High | — |
Q8_0 | 8 | 7.5 GB | Very High | — |
F16 | 16 | 14.3 GB | Maximum | — |
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General
Source: community · 2023-07-29
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
Vicuna 7B (7B parameters) requires approximately 13.9 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, RX 7600 XT 16GB can run Vicuna 7B with a compatibility score of 52/100. It provides 16 GB of memory and achieves approximately 39.1 tokens per second.
The recommended quantization for Vicuna 7B is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for Vicuna 7B: RX 7900 XT 20GB (score: 57/100), RTX A4500 20GB (score: 57/100), RTX 3090 24GB (score: 56/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Vicuna 7B is well-suited for chat as well as instruction. It was designed with these use cases in mind.
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