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Can the NVIDIA GeForce RTX 3090 run LFM2 8B-A1B?

72 ptsGreat (A) · 72 of 100 points: Model quality 17.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At F16 it needs about 18.6 GB of 24.0 GB usable, with an estimated speed of 89.9 tok/s.

Best quant
F16
Generation speed
89.9 tok/s
Prompt processing
17,088 tok/s
First token (1K prompt)
0.3 s
Largest safe context
125K

Download LFM2 8B-A1B

Recommended for the NVIDIA GeForce RTX 3090: F16. 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 F16 file (opens in a new tab)

huggingface.co/LiquidAI/LFM2-8B-A1B-GGUF (opens in a new tab)

ollama run hf.co/LiquidAI/LFM2-8B-A1B-GGUF:F16

Original weights · Hugging Face

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

huggingface.co/LiquidAI/LFM2-8B-A1B (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.17.1 / 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
Total72.1 / 10072 ptsGreat (A) · 72 of 100 points: Model quality 17.1/45, 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 3090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.2 GB 24.0 GB 629 tok/s Runs well 57 ptsUsable (C) · 57 of 100 points: Model quality 12.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.0/15 GGUF files (opens in a new tab)
Q3_K_M 6.0 GB 24.0 GB 495 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 14.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.8/15 GGUF files (opens in a new tab)
Q4_K_M 7.0 GB 24.0 GB 383 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 16.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.7/15 GGUF (opens in a new tab)
Q5_K_M 7.8 GB 24.0 GB 316 tok/s Runs well 64 ptsGood (B) · 64 of 100 points: Model quality 16.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.5/15 GGUF (opens in a new tab)
Q6_K 8.7 GB 24.0 GB 266 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 16.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.4/15 GGUF (opens in a new tab)
Q8_0 10.7 GB 24.0 GB 195 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 17.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.3/15 GGUF (opens in a new tab)
F16 18.6 GB 24.0 GB 89.9 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 17.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 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.