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Can the AMD Radeon RX 7900 XTX run InternLM2 Chat 20B?

73 ptsGreat (A) · 73 of 100 points: Model quality 18.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q5_K_M it needs about 17.5 GB of 24.0 GB usable, with an estimated speed of 42.2 tok/s.

Best quant
Q5_K_M
Generation speed
42.2 tok/s
Prompt processing
3,834 tok/s
First token (1K prompt)
0.3 s
Largest safe context
32K

Download InternLM2 Chat 20B

Recommended for the AMD Radeon RX 7900 XTX: Q5_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q5_K_M file (opens in a new tab)

huggingface.co/mradermacher/internlm2-chat-20b-i1-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/internlm2-chat-20b-i1-GGUF:Q5_K_M

Original weights · Hugging Face

Full-precision files from Shanghai AI Lab, for fine-tuning, vLLM, Transformers.

huggingface.co/internlm/internlm2-chat-20b (opens in a new tab)

No sign-in needed.

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

How the A grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.18.3 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total73.3 / 10073 ptsGreat (A) · 73 of 100 points: Model quality 18.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the AMD Radeon RX 7900 XTX

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 11.4 GB 24.0 GB 83.9 tok/s Runs well 64 ptsGood (B) · 64 of 100 points: Model quality 13.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.9/15 GGUF (opens in a new tab)
Q3_K_M 13.1 GB 24.0 GB 66.1 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 15.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.6/15 GGUF (opens in a new tab)
Q4_K_M 15.5 GB 24.0 GB 51.1 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 18.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.9/15 GGUF (opens in a new tab)
Q5_K_M 17.5 GB 24.0 GB 42.2 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 18.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 19.7 GB 24.0 GB 35.5 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 18.7/45, Memory fit 25.0/25, Speed 14.1/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 24.5 GB 24.0 GB 20.8 tok/s Runs with offload 54 ptsUsable (C) · 54 of 100 points: Model quality 18.9/45, Memory fit 10.0/25, Speed 10.8/15, Memory use 15.0/15 GGUF files (opens in a new tab)
F16 43.1 GB 24.0 GB 1.8 tok/s Heavy offload 41 ptsPoor (D) · 41 of 100 points: Model quality 18.9/45, Memory fit 4.0/25, Speed 3.2/15, Memory use 15.0/15 Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.