Jan-code-4b GGUF size and VRAM requirements
janhq/Jan-code-4b-gguf is a mid-size code-focused language model with 4.41 billion parameters, built on the qwen3 architecture. It is released under the apache-2.0 license and has been downloaded 14,761 times.
To run janhq/Jan-code-4b-gguf locally at a 4,096-token context, its quantized versions need between 3.06 GB (Q3_K_S, lowest quality) and 9.37 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q8_0, needing about 5.52 GB. That means janhq/Jan-code-4b-gguf fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q3_K_S | 3.73 | Fair | 1.91 GB | 0.35 GB | 3.06 GB | 209.1 t/s | Fits in VRAM |
| Q3_K_M | 4.07 | Fair | 2.09 GB | 0.35 GB | 3.24 GB | 191.5 t/s | Fits in VRAM |
| Q3_K_L | 4.36 | Good | 2.24 GB | 0.35 GB | 3.39 GB | 178.4 t/s | Fits in VRAM |
| Q4_0 | 4.69 | Good | 2.41 GB | 0.35 GB | 3.56 GB | 165.9 t/s | Fits in VRAM |
| Q4_K_S | 4.72 | Good | 2.42 GB | 0.35 GB | 3.57 GB | 165.1 t/s | Fits in VRAM |
| Q4_K_M | 4.93 | Good | 2.53 GB | 0.35 GB | 3.68 GB | 158.1 t/s | Fits in VRAM |
| Q4_1 | 5.15 | Very good | 2.64 GB | 0.35 GB | 3.8 GB | 151.2 t/s | Fits in VRAM |
| Q4_K_XL | 5.44 | Very good | 2.79 GB | 0.35 GB | 3.94 GB | 143.2 t/s | Fits in VRAM |
| Q5_0 | 5.61 | Very good | 2.88 GB | 0.35 GB | 4.03 GB | 138.9 t/s | Fits in VRAM |
| Q5_K_S | 5.61 | Very good | 2.88 GB | 0.35 GB | 4.03 GB | 138.9 t/s | Fits in VRAM |
| Q5_K_M | 5.72 | Very good | 2.94 GB | 0.35 GB | 4.09 GB | 136.0 t/s | Fits in VRAM |
| Q5_1 | 6.06 | Very good | 3.11 GB | 0.35 GB | 4.26 GB | 128.5 t/s | Fits in VRAM |
| Q6_K | 6.57 | Excellent | 3.38 GB | 0.35 GB | 4.53 GB | 118.5 t/s | Fits in VRAM |
| Q8_0 | 8.51 | Excellent | 4.37 GB | 0.35 GB | 5.52 GB | 91.5 t/s | Fits in VRAM |
| GGUF | 16.01 | Excellent | 8.22 GB | 0.35 GB | 9.37 GB | 6.1 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 janhq/Jan-code-4b-gguf?
You need about 5.52 GB of VRAM to run janhq/Jan-code-4b-gguf entirely on the GPU using the Q8_0 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run janhq/Jan-code-4b-gguf on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run janhq/Jan-code-4b-gguf fully on the GPU using Q8_0 (about 5.52 GB).
Can I run janhq/Jan-code-4b-gguf on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run janhq/Jan-code-4b-gguf fully on the GPU using GGUF (about 9.37 GB).
Can I run janhq/Jan-code-4b-gguf on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run janhq/Jan-code-4b-gguf fully on the GPU using GGUF (about 9.37 GB).
What is the best quantization for janhq/Jan-code-4b-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.