Model reference · open weights
vicuna is an open-weight language model from lmsys. vicuna-7b-v1.5 (BF16) weighs 13.5 GB; the smallest configuration that runs it is 2× RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the lmsys/vicuna-7b-v1.5 model card, 2026-10-01
What it is
| Released by | lmsys |
|---|---|
| Released | 2023-07-29 |
| VRAM | 13.5 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory |
|---|---|---|---|
| 4K each | |||
| RTX 3060 12 GB | 11.6 GB | ||
| RTX 4060 Ti 16 GB | 2K | 15.4 GB | |
| RTX 3090 24 GB | 4 | all 4K | 23.4 GB |
| RTX 4090 24 GB | 4 | all 4K | 23.4 GB |
| RTX 5090 32 GB | 7 | all 4K | 31.0 GB |
| L40S 48 GB | 13 | all 4K | 44.0 GB |
| A100 80 GB | 29 | all 4K | 78.2 GB |
| H100 80 GB | 28 | all 4K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 35 | all 4K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 42 | all 4K | 107 GB |
| H200 141 GB | 56 | all 4K | 138 GB |
| B200 180 GB | 73 | all 4K | 176 GB |
| 2× RTX 3060 12 GB tensor parallel | 3 | all 4K | 11.6 GB a card |
| 2× RTX 4060 Ti 16 GB tensor parallel | 7 | all 4K | 15.4 GB a card |
| 2× RTX 4090 24 GB tensor parallel | 14 | all 4K | 23.4 GB a card |
| 2× RTX 3090 24 GB tensor parallel | 14 | all 4K | 23.4 GB a card |
| 2× RTX 5090 32 GB tensor parallel | 22 | all 4K | 31.0 GB a card |
| Requests at once | 4K tokens each |
|---|---|
| 1 | 16.2 GB |
| 5 | 24.8 GB |
| 8 | 31.3 GB |
| 16 | 48.5 GB |
| 32 | 82.8 GB |
| 64 | 152 GB |
One card, with vLLM's small-card settings.
From the model card
Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.
The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
Vicuna v1.5 is fine-tuned from Llama 2 with supervised instruction fine-tuning. The training data is around 125K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this paper.
Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this paper and leaderboard.
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.