The Yi 1.5 9B Chat model by 01.AI is a robust language model designed for generating high-quality text, particularly suited for conversational applications. With 9 billion parameters, it offers a balance between computational demands and performance, making it capable of producing coherent and contextually relevant responses. The model supports a context length of 4096 tokens, which is substantial for maintaining context in longer conversations or generating detailed content. It is licensed under the Apache-2.0 license, ensuring flexibility for both commercial and non-commercial use.
In its size class, the Yi 1.5 9B Chat model holds its own, offering efficient performance with a VRAM requirement ranging from 5.5 to 9.2 GB, depending on the quantization used. This makes it a practical choice for users with mid-range GPUs, as it doesn't require top-tier hardware to run effectively. Compared to larger models, it punches above its weight in terms of efficiency, providing a good trade-off between resource usage and output quality. Ideal for developers and hobbyists looking to deploy a chatbot or text generation system locally, this model is well-suited for those who need a powerful yet manageable solution. Realistic hardware requirements include a GPU with at least 6 GB of VRAM, making it accessible to a wide range of users.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 4.963 GB | 5.46 GB | 5.96 GB | 85% |
| Q8_0 | 8 | 8.739 GB | 9.24 GB | 9.74 GB | 98% |
Context window & KV cache
Adds 0.50 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 4K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Yi 1.5 9B Chat
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Easiest. Single command. OpenAI-compatible API on :11434.
Ollama home →- 1
Pull the model
ollama pull yi:9b - 2
Chat
ollama run yi:9b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"yi:9b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Yi 1.5 9B Chat on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Yi 1.5 9B Chatfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
6.7 GB
5.5 GB weights + 0.8 GB KV
Aggregate tok/s
28
across 1 user
Per-user tok/s
28
9 B dense
✅ Fits in 24 GB VRAM with 17.3 GB headroom. Pure-GPU inference — full speed.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run Yi 1.5 9B Chat?
Yi 1.5 9B Chat requires 5.46 GB VRAM minimum with Q4_K_M quantization. For full precision you need 9.24 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Yi 1.5 9B Chat?
To run Yi 1.5 9B Chat, you need a GPU with at least 5.5 GB of VRAM, but 9.2 GB is recommended for optimal performance, especially with higher quantization levels.
Is Yi 1.5 9B Chat good for coding?
Yes, Yi 1.5 9B Chat is suitable for coding tasks due to its strong reasoning capabilities and bilingual support, making it effective for both English and non-English codebases.
Yi 1.5 9B Chat vs Llama 3.1 8B?
Yi 1.5 9B Chat has more parameters (9B vs 8B) and a longer context length (4096 tokens vs typically 2048 tokens), which can result in better performance for complex tasks and longer text sequences.
Can I run Yi 1.5 9B Chat on a Mac?
Yes, you can run Yi 1.5 9B Chat on a Mac, provided your Mac has a compatible GPU with sufficient VRAM. Intel or AMD GPUs with at least 5.5 GB VRAM are recommended.
How much VRAM does Yi 1.5 9B Chat need?
Yi 1.5 9B Chat requires between 5.5 GB and 9.2 GB of VRAM, depending on the quantization level used. Higher quantization levels generally require more VRAM.
Is Yi 1.5 9B Chat censored?
No, Yi 1.5 9B Chat is not censored. It is designed to provide open and uncensored responses, though users should still exercise judgment and responsibility when using the model.
Is Yi 1.5 9B Chat commercial-use allowed?
Yes, Yi 1.5 9B Chat is licensed under Apache-2.0, which allows for commercial use as long as you comply with the terms of the license.
Yi 1.5 9B Chat context length?
The context length for Yi 1.5 9B Chat is 4096 tokens, allowing it to handle longer and more complex text inputs compared to models with shorter context lengths.
Does Yi 1.5 9B Chat support function calling?
Yes, Yi 1.5 9B Chat supports function calling, enabling it to interact with external APIs and perform actions based on user input or generated content.
Yi 1.5 9B Chat quantization options?
Yi 1.5 9B Chat offers multiple quantization options, including 8-bit, 4-bit, and 2-bit, which can reduce the model size and VRAM usage while maintaining performance.
Can Yi 1.5 9B Chat run on CPU?
While Yi 1.5 9B Chat can technically run on a CPU, it is highly recommended to use a GPU for faster inference times and better overall performance.
Yi 1.5 9B Chat fine-tuning?
Yes, Yi 1.5 9B Chat can be fine-tuned on custom datasets to improve its performance on specific tasks or domains. Fine-tuning requires a powerful GPU and sufficient VRAM.
Yi 1.5 9B Chat system requirements?
To run Yi 1.5 9B Chat, you need a system with at least 16 GB of RAM, a GPU with 5.5 GB to 9.2 GB of VRAM, and a modern CPU. SSD storage is recommended for faster loading times.
Yi 1.5 9B Chat performance benchmark?
Performance benchmarks for Yi 1.5 9B Chat vary depending on hardware, but typical inference speeds range from 50 to 150 tokens per second on high-end GPUs like the RTX 3090 or A100.
Yi 1.5 9B Chat for RAG?
Yes, Yi 1.5 9B Chat can be used for Retrieval-Augmented Generation (RAG) tasks, where it can generate responses based on retrieved documents or knowledge bases.
Yi 1.5 9B Chat for agents?
Yi 1.5 9B Chat is well-suited for building conversational agents and chatbots due to its strong reasoning capabilities and bilingual support, making it versatile for various applications.
Yi 1.5 9B Chat for coding vs general?
Yi 1.5 9B Chat performs well for both coding and general tasks, but its strong reasoning and bilingual support make it particularly effective for coding, especially in multilingual environments.
Yi 1.5 9B Chat vs ChatGPT?
Yi 1.5 9B Chat and ChatGPT have different strengths. Yi 1.5 9B Chat offers a longer context length (4096 tokens) and is licensed under Apache-2.0, while ChatGPT may have more extensive training data and a larger parameter count.
Yi 1.5 9B Chat download size?
The download size for Yi 1.5 9B Chat varies depending on the quantization level. The full model is approximately 18 GB, but quantized versions can be as small as 4.5 GB.
Best quant for Yi 1.5 9B Chat?
The best quantization level for Yi 1.5 9B Chat depends on your hardware and performance needs. 4-bit quantization is a good balance between size and performance, offering significant VRAM savings while maintaining high accuracy.