Run MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF locally
MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF is a compact code-focused language model with 1.48 billion parameters, built on the llama architecture. It has been downloaded 163,018 times.
To run MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF locally at a 4,096-token context, its quantized versions need between 2.01 GB (IQ1_S, lowest quality) and 4.3 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 2.57 GB. That means MaziyarPanahi/Yi-Coder-1.5B-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 | 2.66 | Low | 0.46 GB | 0.75 GB | 2.01 GB | 874.4 t/s | Fits in VRAM |
| IQ1_M | 2.76 | Low | 0.47 GB | 0.75 GB | 2.02 GB | 844.5 t/s | Fits in VRAM |
| IQ2_XS | 3.06 | Low | 0.53 GB | 0.75 GB | 2.08 GB | 761.6 t/s | Fits in VRAM |
| Q2_K | 3.44 | Fair | 0.59 GB | 0.75 GB | 2.14 GB | 676.7 t/s | Fits in VRAM |
| IQ3_XS | 3.77 | Fair | 0.65 GB | 0.75 GB | 2.2 GB | 618.0 t/s | Fits in VRAM |
| Q3_K_S | 3.92 | Fair | 0.67 GB | 0.75 GB | 2.22 GB | 593.7 t/s | Fits in VRAM |
| Q3_K_M | 4.26 | Good | 0.73 GB | 0.75 GB | 2.28 GB | 546.6 t/s | Fits in VRAM |
| Q3_K_L | 4.48 | Good | 0.77 GB | 0.75 GB | 2.32 GB | 519.9 t/s | Fits in VRAM |
| IQ4_XS | 4.51 | Good | 0.78 GB | 0.75 GB | 2.33 GB | 515.9 t/s | Fits in VRAM |
| Q4_K_S | 4.9 | Good | 0.84 GB | 0.75 GB | 2.39 GB | 475.0 t/s | Fits in VRAM |
| Q4_K_M | 5.22 | Very good | 0.9 GB | 0.75 GB | 2.45 GB | 445.7 t/s | Fits in VRAM |
| Q5_K_S | 5.7 | Very good | 0.98 GB | 0.75 GB | 2.53 GB | 408.6 t/s | Fits in VRAM |
| Q5_K_M | 5.96 | Very good | 1.02 GB | 0.75 GB | 2.57 GB | 390.4 t/s | Fits in VRAM |
| GGUF | 16.01 | Excellent | 2.75 GB | 0.75 GB | 4.3 GB | 18.2 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-1.5B-Chat-GGUF?
You need about 4.3 GB of VRAM to run MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF entirely on the GPU using the GGUF quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF fully on the GPU using GGUF (about 4.3 GB).
Can I run MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF fully on the GPU using GGUF (about 4.3 GB).
Can I run MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF fully on the GPU using GGUF (about 4.3 GB).
What is the best quantization for MaziyarPanahi/Yi-Coder-1.5B-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.