Model reference · open weights

vicuna

LLMs lmsys Text gen 1 build Open, with conditions 34k dl/mo

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.

  • Vicuna is a 7B parameter auto-regressive language model developed by LMSYS for text generation and chatbot research.
  • It is fine-tuned from Llama 2 using approximately 125K user-shared conversations collected from ShareGPT.
  • The model supports a context length of 4096 tokens and is distributed under the Llama 2 Community License Agreement.

Summary of the lmsys/vicuna-7b-v1.5 model card, 2026-10-01

What it is

Released bylmsys
Released2023-07-29
VRAM13.5 GB for the weights

What it runs on

Memory and cards for vicuna-7b-v1.5 (BF16)

13.5 GBweights, file size
524 MBcache per 1K tokens
620 MBruntime overhead, at least
4,096 tokenscontext max
CardRequests at onceContext maxMemory
4K each
RTX 3060 12 GB11.6 GB
RTX 4060 Ti 16 GB2K15.4 GB
RTX 3090 24 GB4all 4K23.4 GB
RTX 4090 24 GB4all 4K23.4 GB
RTX 5090 32 GB7all 4K31.0 GB
L40S 48 GB13all 4K44.0 GB
A100 80 GB29all 4K78.2 GB
H100 80 GB28all 4K78.1 GB
RTX PRO 6000 Blackwell 96 GB35all 4K93.8 GB
DGX Spark (GB10) 128 GB unified42all 4K107 GB
H200 141 GB56all 4K138 GB
B200 180 GB73all 4K176 GB
2× RTX 3060 12 GB
tensor parallel
3all 4K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
7all 4K15.4 GB a card
2× RTX 4090 24 GB
tensor parallel
14all 4K23.4 GB a card
2× RTX 3090 24 GB
tensor parallel
14all 4K23.4 GB a card
2× RTX 5090 32 GB
tensor parallel
22all 4K31.0 GB a card
Memory needed at each load
Requests at once4K tokens each
116.2 GB
524.8 GB
831.3 GB
1648.5 GB
3282.8 GB
64152 GB

One card, with vLLM's small-card settings.

From the model card

What lmsys says about vicuna

Read the model card

Model Details

Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.

  • Developed by: LMSYS
  • Model type: An auto-regressive language model based on the transformer architecture
  • License: Llama 2 Community License Agreement
  • Finetuned from model: Llama 2

Model Sources

  • Repository: https://github.com/lm-sys/FastChat
  • Blog: https://lmsys.org/blog/2023-03-30-vicuna/
  • Paper: https://arxiv.org/abs/2306.05685
  • Demo: https://chat.lmsys.org/

Uses

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.

How to Get Started with the Model

  • Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights
  • APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api

Training Details

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.

Evaluation

Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this paper and leaderboard.

Difference between different versions of Vicuna

See vicuna_weights_version.md

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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