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Can the NVIDIA GeForce RTX 4060 Ti 16GB run LFM2 24B-A2B?

76 ptsGreat (A) · 76 of 100 points: Model quality 21.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q3_K_M it needs about 13.2 GB of 16.0 GB usable, with an estimated speed of 99.4 tok/s.

Best quant
Q3_K_M
Generation speed
99.4 tok/s
Prompt processing
6,918 tok/s
First token (1K prompt)
0.3 s
Largest safe context
125K

Download LFM2 24B-A2B

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run lfm2:24b

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

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

huggingface.co/LiquidAI/LFM2-24B-A2B-GGUF (opens in a new tab)

ollama run hf.co/LiquidAI/LFM2-24B-A2B-GGUF

Original weights · Hugging Face

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

huggingface.co/LiquidAI/LFM2-24B-A2B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/Liquid4All/cookbook (opens in a new tab)

git clone https://github.com/Liquid4All/cookbook

Liquid AI’s official cookbook for running LFM models.

Links checked 1 week 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.21.2 / 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
Total76.2 / 10076 ptsGreat (A) · 76 of 100 points: Model quality 21.2/45, 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 NVIDIA GeForce RTX 4060 Ti 16GB

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 11.1 GB 16.0 GB 126 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 17.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q3_K_M 13.2 GB 16.0 GB 99.4 tok/s Runs well 76 ptsGreat (A) · 76 of 100 points: Model quality 21.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q4_K_M 16.1 GB 16.0 GB 75.0 tok/s Runs with offload 63 ptsGood (B) · 63 of 100 points: Model quality 23.9/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 18.5 GB 16.0 GB 42.6 tok/s Runs with offload 64 ptsGood (B) · 64 of 100 points: Model quality 24.4/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 21.1 GB 16.0 GB 28.5 tok/s Runs with offload 62 ptsGood (B) · 62 of 100 points: Model quality 24.9/45, Memory fit 10.0/25, Speed 12.7/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 26.8 GB 16.0 GB 16.1 tok/s Heavy offload 53 ptsUsable (C) · 53 of 100 points: Model quality 25.2/45, Memory fit 4.0/25, Speed 9.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 49.2 GB 16.0 GB — Won't run 7 ptsWon't run (F) · 7 of 100 points: Model quality 7.6/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab) 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.