Jan-code-4b GGUF size and VRAM requirements
Jan-code-4b-gguf is a mid-size code-focused language model focused on coding & software engineering. With approximately 4.41 billion parameters, it balances local hardware requirements with reasoning and generation fidelity. It is built on the qwen3 architecture with a native context window of up to 262,144 tokens. The model is primarily recommended for automated code completion, bug triage, unit test generation, refactoring, and multi-file codebases. Released under the apache-2.0 license, it can be executed fully offline without sending data to external APIs.
Under the hood it uses 36 transformer layers, a hidden size of 2,560, 32 attention heads. It uses grouped-query attention (32 query heads sharing 8 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
To run janhq/Jan-code-4b-gguf locally at a 4,096-token context, its quantized versions need between 3.28 GB (Q3_K_S, lowest quality) and 9.59 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included. Context length drives that memory directly: at 4,096 tokens the KV cache for Jan-code-4b-gguf is about 0.56 GB, rising to roughly 36.0 GB at its full 262,144-token context. Shorter prompts free up memory for a higher-quality quantization.
For most users the best balance is Q8_0, needing about 5.73 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.
Available GGUF quantizations for janhq/Jan-code-4b-gguf include Q3_K_S, Q3_K_M, Q3_K_L, Q4_0, Q4_K_S, Q4_K_M, Q4_1, Q4_K_XL, Q5_0, Q5_K_S, Q5_K_M, Q5_1, Q6_K, Q8_0, GGUF. The model supports a native context length of up to 262,144 tokens; a longer context grows the KV cache and the memory needed.
Recommended Use Cases & Local Setup
Ideal for: Automated code completion, bug triage, unit test generation, refactoring, and multi-file codebases.
Direct integration into VS Code and JetBrains IDEs for inline tab completion
Command-line AI pair programmer that automatically applies Git diffs
Local OpenAI-compatible API endpoint serving your editor
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 | 0.56 GB | 3.28 GB | 209.1 t/s | Fits in VRAM |
| Q3_K_M | 4.07 | Fair | 2.09 GB | 0.56 GB | 3.45 GB | 191.5 t/s | Fits in VRAM |
| Q3_K_L | 4.36 | Good | 2.24 GB | 0.56 GB | 3.6 GB | 178.4 t/s | Fits in VRAM |
| Q4_0 | 4.69 | Good | 2.41 GB | 0.56 GB | 3.77 GB | 165.9 t/s | Fits in VRAM |
| Q4_K_S | 4.72 | Good | 2.42 GB | 0.56 GB | 3.79 GB | 165.1 t/s | Fits in VRAM |
| Q4_K_M | 4.93 | Good | 2.53 GB | 0.56 GB | 3.89 GB | 158.1 t/s | Fits in VRAM |
| Q4_1 | 5.15 | Very good | 2.64 GB | 0.56 GB | 4.01 GB | 151.2 t/s | Fits in VRAM |
| Q4_K_XL | 5.44 | Very good | 2.79 GB | 0.56 GB | 4.16 GB | 143.2 t/s | Fits in VRAM |
| Q5_0 | 5.61 | Very good | 2.88 GB | 0.56 GB | 4.24 GB | 138.9 t/s | Fits in VRAM |
| Q5_K_S | 5.61 | Very good | 2.88 GB | 0.56 GB | 4.24 GB | 138.9 t/s | Fits in VRAM |
| Q5_K_M | 5.72 | Very good | 2.94 GB | 0.56 GB | 4.3 GB | 136.0 t/s | Fits in VRAM |
| Q5_1 | 6.06 | Very good | 3.11 GB | 0.56 GB | 4.48 GB | 128.5 t/s | Fits in VRAM |
| Q6_K | 6.57 | Excellent | 3.38 GB | 0.56 GB | 4.74 GB | 118.5 t/s | Fits in VRAM |
| Q8_0 | 8.51 | Excellent | 4.37 GB | 0.56 GB | 5.73 GB | 91.5 t/s | Fits in VRAM |
| GGUF | 16.01 | Excellent | 8.22 GB | 0.56 GB | 9.59 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
What kind of model is janhq/Jan-code-4b-gguf?
janhq/Jan-code-4b-gguf is a code-focused language model with 4.41 billion parameters, based on the qwen3 architecture. It is released under the apache-2.0 license and distributed as GGUF files for local inference.
How much VRAM do you need to run janhq/Jan-code-4b-gguf?
You need about 5.73 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 5.73 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 9.59 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 9.59 GB).
What context length does janhq/Jan-code-4b-gguf support?
janhq/Jan-code-4b-gguf supports a native context length of up to 262,144 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for janhq/Jan-code-4b-gguf?
For janhq/Jan-code-4b-gguf, a strong default is Q4_K_M, which needs about 3.89 GB and keeps most of the quality while roughly halving the memory versus 8-bit. With VRAM to spare, Q5_K_M or Q6_K add a little more quality; if you are tight on memory, a smaller quantization still runs. Pick the highest quantization that fits your VRAM.