Bartowski
internlm JanusCoder 14B (14B parameters) requires approximately 12.0 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 14 GB of VRAM.
Quick specs
Related models
Quick picks
Best hardware
Run this model
Quantization options
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | — |
Q3_K_S | 3 | 6.9 GB | Low | — |
NVFP4 | 4 | 7.8 GB | Medium | — |
Q4_K_M | 4 | 8.5 GB | Medium | — |
Q5_K_M | 5 | 10.1 GB | High | — |
Q6_K | 6 | 11.5 GB | High | — |
Q8_0 | 8 | 15.0 GB | Very High | — |
F16 | 16 | 28.7 GB | Maximum | — |
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
internlm JanusCoder 14B (14B parameters) requires approximately 12.0 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, RX 7600 XT 16GB can run internlm JanusCoder 14B with a compatibility score of 52/100. It provides 16 GB of memory and achieves approximately 19.6 tokens per second.
The recommended quantization for internlm JanusCoder 14B is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for internlm JanusCoder 14B: RTX 5080 Laptop 16GB (score: 56/100), RTX 4080 Super 16GB (score: 56/100), RTX 5080 16GB (score: 56/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, internlm JanusCoder 14B is well-suited for chat as well as coding. It was designed with these use cases in mind.
See also
