Yi Coder 9B Chat
By 01.AI · China
Updated 2026-07-13
Overview
01.AI's 9B code model covering 52 programming languages, hitting 23% on LiveCodeBench — best-in-class under 10B and beating DeepSeek Coder 33B.
When to pick this model
- Code completion and review on a single consumer GPU
- Polyglot codebases spanning many languages
- Self-hosted Copilot alternatives at small scale
- Code workloads needing 128k context for repo-level reasoning
- Cost-sensitive deployments where Qwen Coder is overkill
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 5.5 GB |
| Q5_K_M | 7 GB |
| Q8_0 | 10 GB |
| FP16 (no quantization) | 18 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Yi Coder 9B Chat fits an 8 GB consumer card at Q4_K_M (5.5 GB). Stepping up to Q8_0 nearly doubles the footprint to 10 GB, and unquantized FP16 weights take 18 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Yi Coder 9B Chat needs roughly 12 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 9 tokens/sec on entry-level GPUs, on the order of 28 tokens/sec on a mid-range card, and up to 75 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Yi Coder 9B Chat to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Yi Coder 9B Chat |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (7 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (10 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q8_0 (10 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (18 GB used) |
| 32 GB | RTX 5090 | FP16 (18 GB used) |
Which GPU should you buy to run Yi Coder 9B Chat?
To run Yi Coder 9B Chat locally at Q4, you need ~5.5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| LiveCodeBench | 23 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- LiveCodeBench 23% leads the sub-10B field
- Outperforms DeepSeek Coder 33B at a fraction of the size
- Coverage across 52 programming languages
- Apache 2.0 license
- 128k context enables repo-scale code understanding
Limitations
- Less popular than Qwen Coder, so fewer fine-tunes exist
- No instruction-tuned variant beyond chat
- Quality gap versus Qwen 2.5 Coder 7B/14B in 2025
Typical workloads
In our catalog grid, Yi Coder 9B Chat is filed under Efficient 9B Code — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline).
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense 9B code · Llama base · 128k ctx
Training: 52 programming languages.
The strongest sub-10B code model in its release window — still a sharp pick when you need 128k context on modest hardware.
Quick start
ollama run yi-coder:9bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Yi Coder 9B Chat need?
At the recommended Q4_K_M quantization, Yi Coder 9B Chat needs about 5.5 GB of VRAM. Q8_0 takes 10 GB, and unquantized FP16 weights take 18 GB.
Can Yi Coder 9B Chat run without a GPU?
Yes — with roughly 12 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Yi Coder 9B Chat support?
Yi Coder 9B Chat supports a 125k-token context window (128,000 tokens).
Can I use Yi Coder 9B Chat commercially?
Yes. Yi Coder 9B Chat is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Yi Coder 9B Chat on consumer hardware?
Our compatibility engine estimates on the order of 28 tokens/sec on a mid-range GPU and up to 75 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Yi Coder 9B Chat should I download first?
Start with Q4_K_M (5.5 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.