Model reference · open weights
vicuna-delta is an open-weight language model from lmsys. vicuna-13b-delta-v0 (BF16) weighs 26.0 GB; the smallest configuration that runs it is 2× RTX 4060 Ti 16 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the lmsys/vicuna-13b-delta-v0 model card, 2026-10-01
What it is
| Released by | lmsys |
|---|---|
| Released | 2023-04-03 |
| VRAM | 26.0 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory |
|---|---|---|---|
| 2K each | |||
| RTX 3060 12 GB … RTX 4090 24 GB 4 smaller cards | |||
| RTX 5090 32 GB | 2 | all 2K | 31.0 GB |
| L40S 48 GB | 10 | all 2K | 44.0 GB |
| A100 80 GB | 30 | all 2K | 78.2 GB |
| H100 80 GB | 28 | all 2K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 38 | all 2K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 46 | all 2K | 107 GB |
| H200 141 GB | 64 | all 2K | 138 GB |
| B200 180 GB | 86 | all 2K | 176 GB |
| 2× RTX 4060 Ti 16 GB tensor parallel | 2 | all 2K | 15.4 GB a card |
| 2× RTX 4090 24 GB tensor parallel | 11 | all 2K | 23.4 GB a card |
| 2× RTX 3090 24 GB tensor parallel | 11 | all 2K | 23.4 GB a card |
| 2× RTX 5090 32 GB tensor parallel | 20 | all 2K | 31.0 GB a card |
| Requests at once | 2K tokens each |
|---|---|
| 1 | 28.4 GB |
| 5 | 35.1 GB |
| 8 | 40.1 GB |
| 16 | 53.5 GB |
| 32 | 80.4 GB |
| 64 | 134 GB |
One card, with vLLM's small-card settings.
From the model card
NOTE: New version available Please check out a newer version of the weights here.
NOTE: This "delta model" cannot be used directly. Users have to apply it on top of the original LLaMA weights to get actual Vicuna weights. See instructions.
Vicuna is a chat assistant trained by fine-tuning LLaMA 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.
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.
Vicuna v0 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 70K 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.