Model reference · open weights
Yi is an open-weight language model from 01-ai. Yi-34B-Chat (BF16) weighs 68.8 GB; the smallest configuration that runs it is H100 80 GB.
Summary of the 01-ai/Yi-6B model card, 2026-10-01. The estimate below is for another build of the family.
What it is
| Released by | 01-ai |
|---|---|
| Released | 2023-11-01 |
| Parameters | 6.1B |
| VRAM | 68.8 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory |
|---|---|---|---|
| 4K each | |||
| RTX 3060 12 GB … L40S 48 GB 6 smaller cards | — | — | |
| A100 80 GB | 8 | all 4K | 78.2 GB |
| H100 80 GB | 4 | all 4K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 20 | all 4K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 33 | all 4K | 107 GB |
| H200 141 GB | 64 | all 4K | 138 GB |
| B200 180 GB | 101 | all 4K | 176 GB |
| 2× L40S 48 GB tensor parallel | 17 | all 4K | 44.0 GB a card |
| 4× RTX 4090 24 GB tensor parallel | 21 | all 4K | 23.4 GB a card |
| 4× RTX 3090 24 GB tensor parallel | 21 | all 4K | 23.4 GB a card |
| 4× RTX 5090 32 GB tensor parallel | 51 | all 4K | 31.0 GB a card |
| 2× H100 80 GB tensor parallel | 77 | all 4K | 78.1 GB a card |
| 2× A100 80 GB tensor parallel | 85 | all 4K | 78.2 GB a card |
| Requests at once | 4K tokens each |
|---|---|
| 1 | 70.6 GB |
| 5 | 74.6 GB |
| 8 | 77.6 GB |
| 16 | 85.7 GB |
| 32 | 102 GB |
| 64 | 134 GB |
One card, with vLLM's small-card settings.
Builds
From the model card
🤗 Hugging Face • 🤖 ModelScope • ✡️ WiseModel
👩🚀 Ask questions or discuss ideas on GitHub
📝 Check out Yi Tech Report
📚 Grow at Yi Learning Hub
🤖 The Yi series models are the next generation of open-source large language models trained from scratch by 01.AI.
🙌 Targeted as a bilingual language model and trained on 3T multilingual corpus, the Yi series models become one of the strongest LLM worldwide, showing promise in language understanding, commonsense reasoning, reading comprehension, and more. For example,
Yi-34B-Chat model landed in second place (following GPT-4 Turbo), outperforming other LLMs (such as GPT-4, Mixtral, Claude) on the AlpacaEval Leaderboard (based on data available up to January 2024).
Yi-34B model ranked first among all existing open-source models (such as Falcon-180B, Llama-70B, Claude) in both English and Chinese on various benchmarks, including Hugging Face Open LLM Leaderboard (pre-trained) and C-Eval (based on data available up to November 2023).
🙏 (Credits to Llama) Thanks to the Transformer and Llama open-source communities, as they reduce the efforts required to build from scratch and enable the utilization of the same tools within the AI ecosystem.
💡 TL;DR
The Yi series models adopt the same model architecture as Llama but are NOT derivatives of Llama.
Both Yi and Llama are based on the Transformer structure, which has been the standard architecture for large language models since 2018.
Grounded in the Transformer architecture, Llama has become a new cornerstone for the majority of state-of-the-art open-source models due to its excellent stability, reliable convergence, and robust compatibility. This positions Llama as the recognized foundational framework for models including Yi.
Thanks to the Transformer and Llama architectures, other models can leverage their power, reducing the effort required to build from scratch and enabling the utilization of the same tools within their ecosystems.
However, the Yi series models are NOT derivatives of Llama, as they do not use Llama's weights.
As Llama's structure is employed by the majority of open-source models, the key factors of determining model performance are training datasets, training pipelines, and training infrastructure.
Developing in a unique and proprietary way, Yi has independently created its own high-quality training datasets, efficient training pipelines, and robust training infrastructure entirely from the ground up. This effort has led to excellent performance with Yi series models ranking just behind GPT4 and surpassing Llama on the Alpaca Leaderboard in Dec 2023.
In the "Needle-in-a-Haystack" test, the Yi-34B-200K's performance is improved by 10.5%, rising from 89.3% to an impressive 99.8%. We continue to pre-train the model on 5B tokens long-context data mixture and demonstrate a near-all-green performance.
Yi-34B-ChatYi-34B-Chat-4bitsYi-34B-Chat-8bitsYi-6B-ChatYi-6B-Chat-4bitsYi-6B-Chat-8bitsYou can try some of them interactively at:
release, except that the context window is extended to 200K.
with the parameter sizes of 6B and 34B. Both of them are trained with 4K sequence length and can be extended to 32K during inference time.
Yi models come in multiple sizes and cater to different use cases. You can also fine-tune Yi models to meet your specific requirements.
If you want to deploy Yi models, make sure you meet the software and hardware requirements.
| Model | Download |
|---|---|
| Yi-34B-Chat | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-34B-Chat-4bits | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-34B-Chat-8bits | • 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel |
| Yi-6B-Chat | • 🤗 Hugging Face • [🤖 ModelScope](https://www.models |
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.