01.AI
Yi Coder 9B (9B parameters) requires approximately 8.8 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 11 GB of VRAM.
Get started
— copy & paste to run locallyCopy-paste commands to run Yi Coder 9B on your machine.
Run
lms load Yi-Coder-9B-Chat && lms server startQuick specs
About this model
Related models
Quick picks
Best hardware
Run this model
Quantization options
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | — |
Q3_K_S | 3 | 4.4 GB | Low | — |
NVFP4 | 4 | 5.0 GB | Medium | — |
Q4_K_M | 4 | 5.5 GB | Medium | — |
Q5_K_M | 5 | 6.5 GB | High | — |
Q6_K | 6 | 7.4 GB | High | — |
Q8_0 | 8 | 9.6 GB | Very High | — |
F16 | 16 | 18.5 GB | Maximum | — |
Quality benchmarks
Coding
Reasoning
General
Source: official · 2024-09-04
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
Yi Coder 9B (9B parameters) requires approximately 8.8 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc B570 10GB can run Yi Coder 9B with a compatibility score of 63/100. It provides 10 GB of memory and achieves approximately 40.6 tokens per second.
The recommended quantization for Yi Coder 9B is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for Yi Coder 9B: RTX 3080 Ti 12GB (score: 69/100), RTX 3080 12GB (score: 69/100), RTX 5070 12GB (score: 69/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Yi Coder 9B is well-suited for code as well as chat. It was designed with these use cases in mind.
See also
