Run MaziyarPanahi/Yi-Coder-9B-Chat-GGUF locally
MaziyarPanahi/Yi-Coder-9B-Chat-GGUF is a large code-focused language model with 8.83 billion parameters, built on the llama architecture. It has been downloaded 154,665 times.
To run MaziyarPanahi/Yi-Coder-9B-Chat-GGUF locally at a 4,096-token context, its quantized versions need between 3.05 GB (IQ1_S, lowest quality) and 17.62 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q5_K_M, needing about 7.0 GB. That means MaziyarPanahi/Yi-Coder-9B-Chat-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| IQ1_S | 1.83 | Very low | 1.88 GB | 0.38 GB | 3.05 GB | 213.2 t/s | Fits in VRAM |
| IQ1_M | 1.98 | Very low | 2.03 GB | 0.38 GB | 3.21 GB | 196.9 t/s | Fits in VRAM |
| IQ2_XS | 2.45 | Very low | 2.52 GB | 0.38 GB | 3.7 GB | 158.6 t/s | Fits in VRAM |
| Q2_K | 3.04 | Low | 3.12 GB | 0.38 GB | 4.3 GB | 128.0 t/s | Fits in VRAM |
| IQ3_XS | 3.37 | Fair | 3.46 GB | 0.38 GB | 4.64 GB | 115.5 t/s | Fits in VRAM |
| Q3_K_S | 3.53 | Fair | 3.63 GB | 0.38 GB | 4.81 GB | 110.1 t/s | Fits in VRAM |
| Q3_K_M | 3.92 | Fair | 4.03 GB | 0.38 GB | 5.2 GB | 99.3 t/s | Fits in VRAM |
| Q3_K_L | 4.25 | Good | 4.37 GB | 0.38 GB | 5.54 GB | 91.6 t/s | Fits in VRAM |
| IQ4_XS | 4.34 | Good | 4.46 GB | 0.38 GB | 5.63 GB | 89.8 t/s | Fits in VRAM |
| Q4_K_S | 4.6 | Good | 4.72 GB | 0.38 GB | 5.9 GB | 84.7 t/s | Fits in VRAM |
| Q4_K_M | 4.83 | Good | 4.96 GB | 0.38 GB | 6.14 GB | 80.6 t/s | Fits in VRAM |
| Q5_K_S | 5.53 | Very good | 5.69 GB | 0.38 GB | 6.86 GB | 70.3 t/s | Fits in VRAM |
| Q5_K_M | 5.67 | Very good | 5.83 GB | 0.38 GB | 7.0 GB | 68.6 t/s | Fits in VRAM |
| GGUF | 16.0 | Excellent | 16.45 GB | 0.38 GB | 17.62 GB | 3.0 t/s | Offload |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run MaziyarPanahi/Yi-Coder-9B-Chat-GGUF?
You need about 5.9 GB of VRAM to run MaziyarPanahi/Yi-Coder-9B-Chat-GGUF entirely on the GPU using the Q4_K_S quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/Yi-Coder-9B-Chat-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run MaziyarPanahi/Yi-Coder-9B-Chat-GGUF fully on the GPU using Q5_K_M (about 7.0 GB).
Can I run MaziyarPanahi/Yi-Coder-9B-Chat-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/Yi-Coder-9B-Chat-GGUF fully on the GPU using Q5_K_M (about 7.0 GB).
Can I run MaziyarPanahi/Yi-Coder-9B-Chat-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Yi-Coder-9B-Chat-GGUF fully on the GPU using GGUF (about 17.62 GB).
What is the best quantization for MaziyarPanahi/Yi-Coder-9B-Chat-GGUF?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.