LFM2 24B-A2B on NVIDIA RTX PRO 6000 Blackwell

Can the NVIDIA RTX PRO 6000 Blackwell run LFM2 24B-A2B?

77 pts

Yes, it runs well

Runs well. At F16 it needs about 53.2 GB of 96.0 GB usable, with an estimated speed of 112 tok/s.

Best quant: F16
Generation speed: 112 tok/s
Prompt processing: 39,130 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 125K

Download LFM2 24B-A2B

Recommended for the NVIDIA RTX PRO 6000 Blackwell: F16. Every other quantization has its own download link in the table below.

Quickest · Ollama

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

ollama run lfm2:24b-bf16

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.

Download the F16 file (opens in a new tab)

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

ollama run hf.co/LiquidAI/LFM2-24B-A2B-GGUF:BF16

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.	25.2 / 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.	12.8 / 15
Total	78.0 / 100	77 ptsGreat (A) · 77 of 100 points: Model quality 25.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.8/15

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

Every quantization on the NVIDIA RTX PRO 6000 Blackwell

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	15.1 GB	96.0 GB	785 tok/s	Runs well	61 ptsGood (B) · 61 of 100 points: Model quality 17.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.6/15	GGUF files (opens in a new tab)
`Q3_K_M`	17.2 GB	96.0 GB	618 tok/s	Runs well	65 ptsGood (B) · 65 of 100 points: Model quality 21.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.1/15	GGUF files (opens in a new tab)
`Q4_K_M`	20.1 GB	96.0 GB	478 tok/s	Runs well	68 ptsGood (B) · 68 of 100 points: Model quality 23.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.8/15	GGUF (opens in a new tab)
`Q5_K_M`	22.5 GB	96.0 GB	395 tok/s	Runs well	69 ptsGood (B) · 69 of 100 points: Model quality 24.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.4/15	GGUF (opens in a new tab)
`Q6_K`	25.1 GB	96.0 GB	332 tok/s	Runs well	70 ptsGood (B) · 70 of 100 points: Model quality 24.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.0/15	GGUF (opens in a new tab)
`Q8_0`	30.8 GB	96.0 GB	243 tok/s	Runs well	72 ptsGreat (A) · 72 of 100 points: Model quality 25.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.4/15	GGUF (opens in a new tab)
`F16`	53.2 GB	96.0 GB	112 tok/s	Runs well	77 ptsGreat (A) · 77 of 100 points: Model quality 25.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.8/15	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

79 ptsGreat (A) · 79 of 100 points: Model quality 24.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 Q6_K · 332 tok/s
79 ptsGreat (A) · 79 of 100 points: Model quality 24.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.9/15 Apple M4 Pro (48 GB) Q6_K · 43.5 tok/s
79 ptsGreat (A) · 79 of 100 points: Model quality 25.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.4/15 Apple M1 Max (64 GB) Q8_0 · 46.8 tok/s
79 ptsGreat (A) · 79 of 100 points: Model quality 25.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.4/15 Apple M4 Max (64 GB) Q8_0 · 63.9 tok/s
79 ptsGreat (A) · 79 of 100 points: Model quality 24.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX Q5_K_M · 182 tok/s
79 ptsGreat (A) · 79 of 100 points: Model quality 24.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 Q5_K_M · 206 tok/s

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.