Yi Coder 9B Chat — Hardware Requirements & GPU Compatibility
ChatCode
Yi-Coder 9B Chat is 01.AI's 9-billion-parameter instruction-tuned model for code, built upon the Yi-Coder-9B base checkpoint. The card claims support for 52 programming languages and lists a 23% pass rate on LiveCodeBench; it notes this is the only model under 10 billion parameters to hit 20%. Its focus is coding assistance, code completion, and long-file understanding instead of general chat. At this size, quantization lets it run on a single consumer GPU, and even mid-range cards handle a 4-bit build. The model handles a 131,072 token context window, sufficient for large source files or multiple ones at once. Released under the Apache 2.0 license, it allows unrestricted commercial and research use. Published in August 2024, it joins a 1.5B chat model and base versions of both sizes within the series.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~113 tok/s · 128K)Macs from 8 GB (IQ2_XXS · ~25 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Yi Coder 9B Chat
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run yi-coder:9b-chat-q4_K_MQ4_K_M fits fully on the RTX 4090 · 16.8 GB free · 16K context
Download: Yi-Coder-9B-Chat.Q4_K_M.gguf5.33 GBMaziyarPanahi
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~102 tok/s
- generation speed
- Cost per 1M output tokens
- $0.33
- per 1M output tokens
How Much VRAM Does Yi Coder 9B Chat Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.3 GB | 7.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~154 tok/s | |
| 4.4 GB | 7.4 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~150 tok/s | |
| 4.8 GB | 7.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~136 tok/s | |
| 4.9 GB | 7.9 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 128K context · ~133 tok/s | |
| 5.8 GB | 8.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~113 tok/s | |
| 6.8 GB | 9.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~97 tok/s | |
| 7.8 GB | 10.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~84 tok/s | |
| 9.3 GB | 12.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~70 tok/s |
Selected Q4_K_M
4-bit medium quantization7.2GBat 16K context
- Weights
- 5.3 GB
- KV cache
- 1.6 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.8 GB free · ~113 tok/s
≥50 tok/s needs ≥446 GB/s · RTX 5090 ~201, RTX 3090 Ti ~113
Which Hardware Can Run Yi Coder 9B Chat?
Q4_K_M · 5.8 GBYi Coder 9B Chat (Q4_K_M) requires 5.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 131K context window can add up to 12.7 GB, bringing total usage to 18.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Yi Coder 9B Chat, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.8 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~85Simulate output on RTX 3080RTX 2080 Ti~69Simulate output on RTX 2080 TiRTX 3070 Ti~68Simulate output on RTX 3070 TiOrin Nano~11Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.8 GB free · up to 128K ctx
1 GPU
RX 7900 XT~83Simulate output on RX 7900 XT32–40 GB
fits · 24.8 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~201Simulate output on RTX 5090M5 Max36 GB~56Simulate output on Mac Studio M5 Max (36 GB)M532 GB~19Simulate output on MacBook Air 13" M5 (32 GB)Orin~23Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~15Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Yi Coder 9B Chat
Yi family · 6 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| Ornith 1.0 9BDeep Reinforce | — | 6.5 GB | ~106 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
Specifications
- Publisher
- 01.AI
- Family
- Yi
- Parameters
- 8.8B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 64,000
- Release Date
- 2024-08-21
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Yi Coder 9B Chat need?
Yi Coder 9B Chat requires 5.8 GB of VRAM at Q4_K_M, or 18.2 GB at BF16. Full 131K context adds up to 12.7 GB (18.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.8B × 4.8 bits ÷ 8 = 5.3 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 13.2 GB (at full 131K context)
VRAM usage by quantization
What's the best quantization for Yi Coder 9B Chat?
For Yi Coder 9B Chat, Q4_K_M (5.8 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.9 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Yi Coder 9B Chat on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Yi Coder 9B Chat, the smallest at IQ2_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.8 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run Yi Coder 9B Chat locally?
Yes — Yi Coder 9B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Yi Coder 9B Chat?
At Q4_K_M, Yi Coder 9B Chat can reach ~828 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~113 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 5.8 × 0.65 = ~897 tok/s
Estimated speed at Q4_K_M (5.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Yi Coder 9B Chat?
At Q4_K_M, the download is about 5.30 GB. The full-precision BF16 version is 17.66 GB. The smallest option (IQ2_XXS) is 2.43 GB.
Which GPUs can run Yi Coder 9B Chat?
52 consumer GPUs can run Yi Coder 9B Chat at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Yi Coder 9B Chat?
151 devices with unified memory can run Yi Coder 9B Chat at Q4_K_M (5.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.