Model reference · open weights

Yi

LLMs 01-ai Text gen 3 builds Open weights 32k dl/mo

Yi is an open-weight language model from 01-ai. Yi-9B (BF16) weighs 17.7 GB; the smallest configuration that runs it is 2× RTX 3060 12 GB.

  • Yi is an open-source large language model developed by 01-ai for text generation tasks.
  • The specific Yi-6B variant contains 6.1 billion parameters and supports a context length of 4096 tokens.
  • It is designed as a bilingual model trained on a multilingual corpus and is distributed under the Apache 2.0 license.

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 by01-ai
Released2023-11-01
Parameters6.1B
VRAM17.7 GB for the weights

What it runs on

Memory and cards for Yi-9B (BF16)

17.7 GBweights, file size
98 MBcache per 1K tokens
652 MBruntime overhead, at least
4,096 tokenscontext max
CardRequests at onceContext maxMemory
4K each
RTX 3060 12 GB … RTX 4060 Ti 16 GB
2 smaller cards
——
RTX 3090 24 GB12all 4K23.4 GB
RTX 4090 24 GB12all 4K23.4 GB
RTX 5090 32 GB31all 4K31.0 GB
L40S 48 GB63all 4K44.0 GB
A100 80 GB148all 4K78.2 GB
H100 80 GB140all 4K78.1 GB
RTX PRO 6000 Blackwell 96 GB179all 4K93.8 GB
DGX Spark (GB10) 128 GB unified212all 4K107 GB
H200 141 GB288all 4K138 GB
B200 180 GB382all 4K176 GB
2× RTX 3060 12 GB
tensor parallel
10all 4K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
29all 4K15.4 GB a card
Memory needed at each load
Requests at once4K tokens each
118.7 GB
520.3 GB
821.5 GB
1624.8 GB
3231.2 GB
6444.1 GB

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

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
Yi-6B ↗ 6.1BBF16 12.1 GBRTX 4060 Ti 16 GB
Yi-34B-Chat ↗ 34.4BBF16 68.8 GBH100 80 GB
Yi-9B (above) ↗ 8.8BBF16 17.7 GB2× RTX 3060 12 GB

From the model card

What 01-ai says about Yi

Read the model card

🤗 Hugging Face • 🤖 ModelScope • ✡️ WiseModel

👩‍🚀 Ask questions or discuss ideas on  GitHub

📝 Check out   Yi Tech Report

📚 Grow at  Yi Learning Hub

What is Yi?

Introduction

  • 🤖 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.

News

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-Chat
  • Yi-34B-Chat-4bits
  • Yi-34B-Chat-8bits
  • Yi-6B-Chat
  • Yi-6B-Chat-4bits
  • Yi-6B-Chat-8bits

You 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.

Models

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.

Chat models

ModelDownload
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.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms