InternLM
InternLM 20B (20B parameters) requires approximately 34.2 GB of VRAM with Q5_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 40 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run InternLM 20B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "internlm/internlm2_5-20b-chat" \
--hf-file "internlm2_5-20b-chat-Q5_K_M.gguf" \
-c 4096 -ngl 99Quick specs
About this model
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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 | — |
Quality benchmarks
Reasoning
General
Source: community · 2025-01-01
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
InternLM 20B (20B parameters) requires approximately 34.2 GB of VRAM with Q5_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Mac mini M4 64GB can run InternLM 20B with a compatibility score of 54/100. It provides 64 GB of memory and achieves approximately 8.0 tokens per second.
The recommended quantization for InternLM 20B is Q5_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 20B: RTX PRO 5000 Blackwell 48GB (score: 64/100), RTX 6000 Ada 48GB (score: 63/100), AMD Instinct MI210 64GB (score: 63/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, InternLM 20B is well-suited for chat as well as coding. It was designed with these use cases in mind.
See also
