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Can the NVIDIA GeForce RTX 4060 run InternLM3 8B Instruct?

75 ptsGreat (A) · 75 of 100 points: Model quality 21.4/45, Memory fit 25.0/25, Speed 14.6/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 6.8 GB of 8.0 GB usable, with an estimated speed of 37.9 tok/s.

Best quant
Q4_K_M
Generation speed
37.9 tok/s
Prompt processing
1,235 tok/s
First token (1K prompt)
0.8 s
Largest safe context
32K

Download InternLM3 8B Instruct

Recommended for the NVIDIA GeForce RTX 4060: Q4_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 Q4_K_M file (opens in a new tab)

huggingface.co/internlm/internlm3-8b-instruct-GGUF (opens in a new tab)

ollama run hf.co/internlm/internlm3-8b-instruct-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/internlm/internlm3-8b-instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Shanghai AI Lab. The weights themselves download from Hugging Face.

github.com/InternLM/InternLM (opens in a new tab)

git clone https://github.com/InternLM/InternLM

Links checked 7 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.21.4 / 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.14.6 / 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
Total76.0 / 10075 ptsGreat (A) · 75 of 100 points: Model quality 21.4/45, Memory fit 25.0/25, Speed 14.6/15, 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 NVIDIA GeForce RTX 4060

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 4.9 GB 8.0 GB 62.3 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 15.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.2/15 GGUF (opens in a new tab)
Q3_K_M 5.7 GB 8.0 GB 49.1 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 18.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 6.8 GB 8.0 GB 37.9 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 21.4/45, Memory fit 25.0/25, Speed 14.6/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 7.7 GB 8.0 GB 31.3 tok/s Tight fit 74 ptsGreat (A) · 74 of 100 points: Model quality 21.8/45, Memory fit 24.0/25, Speed 13.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 8.6 GB 8.0 GB 20.8 tok/s Runs with offload 58 ptsGood (B) · 58 of 100 points: Model quality 22.3/45, Memory fit 10.0/25, Speed 10.8/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 10.7 GB 8.0 GB 10.2 tok/s Runs with offload 55 ptsUsable (C) · 55 of 100 points: Model quality 22.5/45, Memory fit 10.0/25, Speed 7.6/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 19.0 GB 8.0 GB 3.0 tok/s Heavy offload 45 ptsUsable (C) · 45 of 100 points: Model quality 22.5/45, Memory fit 4.0/25, Speed 4.1/15, 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.