FLUX.2 [klein] 9B on NVIDIA GeForce RTX 2060

Can the NVIDIA GeForce RTX 2060 run FLUX.2 [klein] 9B?

74 pts

Yes, with little memory to spare

Tight fit. At FP8 it needs about 5.6 GB of 6.0 GB usable, with an estimated speed of 14 s.

Best precision: FP8
Memory mode: Sequential CPU offload
Time per image: 14 s
Output: 1024×1024, 4 steps

Download FLUX.2 [klein] 9B

Quantized files · GGUF for ComfyUI

Smaller GGUF versions for ComfyUI (with the ComfyUI-GGUF node). No sign-in needed; the model’s license still applies.

huggingface.co/unsloth/FLUX.2-klein-9B-GGUF (opens in a new tab)

Original weights · Hugging Face

Full-precision files from Black Forest Labs, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/black-forest-labs/FLUX.2-klein-9B (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

Official repo · GitHub

Reference code, inference scripts and docs from Black Forest Labs. The weights themselves download from Hugging Face.

github.com/black-forest-labs/flux2 (opens in a new tab)

git clone https://github.com/black-forest-labs/flux2

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.	37.0 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	12.6 / 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
Total	74.6 / 100	74 ptsGreat (A) · 74 of 100 points: Model quality 37.0/45, Memory fit 10.0/25, Speed 12.6/15, Memory use 15.0/15

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

Every precision on the NVIDIA GeForce RTX 2060

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	8.8 GB	6.0 GB	—	—	Won't run	11 ptsWon't run (F) · 11 of 100 points: Model quality 11.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	Original weights (opens in a new tab)
`FP8`	5.6 GB	6.0 GB	14 s	Sequential CPU offload	Tight fit	74 ptsGreat (A) · 74 of 100 points: Model quality 37.0/45, Memory fit 10.0/25, Speed 12.6/15, Memory use 15.0/15	Original weights (opens in a new tab)
`Q8 GGUF`	5.8 GB	6.0 GB	—	—	Won't run	11 ptsWon't run (F) · 11 of 100 points: Model quality 11.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF (opens in a new tab)
`Q4 GGUF`	4.3 GB	6.0 GB	19 s	Sequential CPU offload	Runs well	73 ptsGreat (A) · 73 of 100 points: Model quality 35.2/45, Memory fit 12.0/25, Speed 10.9/15, Memory use 15.0/15	GGUF (opens in a new tab)

Other hardware that runs it

92 ptsExcellent (S) · 92 of 100 points: Model quality 37.8/45, Memory fit 25.0/25, Speed 14.6/15, Memory use 15.0/15 Apple M4 Max (64 GB) FP16 · 11 s
92 ptsExcellent (S) · 92 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.8/15 NVIDIA GeForce RTX 5090 Q8 GGUF · 1.7 s
92 ptsExcellent (S) · 92 of 100 points: Model quality 37.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX FP8 · 1.8 s
92 ptsExcellent (S) · 92 of 100 points: Model quality 37.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 FP8 · 3.0 s
92 ptsExcellent (S) · 92 of 100 points: Model quality 37.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 FP8 · 1.6 s
89 ptsExcellent (S) · 89 of 100 points: Model quality 37.8/45, Memory fit 25.0/25, Speed 11.8/15, Memory use 15.0/15 Apple M1 Max (64 GB) FP16 · 16 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.