Bartowski
internlm2 5 20b chat (20B parameters) requires approximately 16.3 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 19 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 | 7.8 GB | Low | — |
Q3_K_S | 3 | 9.8 GB | Low | — |
NVFP4 | 4 | 11.2 GB | Medium | — |
Q4_K_M | 4 | 12.2 GB | Medium | — |
Q5_K_M | 5 | 14.4 GB | High | — |
Q6_K | 6 | 16.4 GB | High | — |
Q8_0 | 8 | 21.4 GB | Very High | — |
F16 | 16 | 41.0 GB | Maximum | — |
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
internlm2 5 20b chat (20B parameters) requires approximately 16.3 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc Pro B60 24GB can run internlm2 5 20b chat with a compatibility score of 52/100. It provides 24 GB of memory and achieves approximately 20.2 tokens per second.
The recommended quantization for internlm2 5 20b chat 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 internlm2 5 20b chat: RTX 4090 24GB (score: 55/100), RTX 5090 Laptop 24GB (score: 55/100), NVIDIA A30 24GB (score: 55/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, internlm2 5 20b chat is well-suited for chat. It was designed with these use cases in mind.
See also
