FLUX.1 [schnell] on NVIDIA RTX PRO 2000 Blackwell Generation Laptop GPU

Can the NVIDIA RTX PRO 2000 Blackwell Generation Laptop GPU run FLUX.1 [schnell]?

Yes, it runs great (CPU offload)

Runs great (CPU offload). At Q4 GGUF it needs about 5.1 GB of 8.0 GB usable and takes about 9.9 s per image.

Best precision: Q4 GGUF
Memory mode: Sequential CPU offload
Time per image: 9.9 s
Output: 1024×1024, 4 steps

Best app to run it on the NVIDIA RTX PRO 2000 Blackwell Generation Laptop GPU

On Windows

ComfyUI

Desktop app · runs on PyTorch (ComfyUI), GGUF via ComfyUI-GGUF

Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Get ComfyUI

On Linux

ComfyUI

Desktop app · runs on PyTorch (ComfyUI), GGUF via ComfyUI-GGUF

Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Get ComfyUI

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download FLUX.1 [schnell]

Recommended for the NVIDIA RTX PRO 2000 Blackwell Generation Laptop GPU: q4_gguf. Every other precision has its own download link in the table below.

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.

Download the q4_gguf (Q4_K_S) file (opens in a new tab)

huggingface.co/city96/FLUX.1-schnell-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.1-schnell (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/flux (opens in a new tab)

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

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 65Good quality (B) · 65 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q4 GGUF, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs greatCPU offload: How well it runs on the NVIDIA RTX PRO 2000 Blackwell Generation Laptop GPU: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	29.3 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.	12.0 / 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.	14.8 / 15
Match score	71.1 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every precision on the NVIDIA RTX PRO 2000 Blackwell Generation Laptop GPU

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	11.0 GB	8.0 GB	—	—	Won't run	70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	6.8 GB	8.0 GB	7.4 s	Sequential CPU offload	Runs welltight fit · CPU offload	68Good quality (B) · 68 of 100: how capable the model is at FP8, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`Q8 GGUF`	7.1 GB	8.0 GB	9.5 s	Sequential CPU offload	Runs welltight fit · CPU offload	69Good quality (B) · 69 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4 GGUF`	5.1 GB	8.0 GB	9.9 s	Sequential CPU offload	Runs greatCPU offload	65Good quality (B) · 65 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`NF4`	5.0 GB	8.0 GB	9.1 s	Sequential CPU offload	Runs greatCPU offload	65Good quality (B) · 65 of 100: how capable the model is at NF4, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

69Good quality (B) · 69 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q8 GGUF · 1.9 s · Runs great
68Good quality (B) · 68 of 100: how capable the model is at FP8, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX FP8 · 2.0 s · Runs great
68Good quality (B) · 68 of 100: how capable the model is at FP8, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 FP8 · 3.4 s · Runs great
68Good quality (B) · 68 of 100: how capable the model is at FP8, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 FP8 · 1.8 s · Runs great
70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 Max (64 GB) FP16 · 18 s · Runs well
65Good quality (B) · 65 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q4 GGUF · 3.2 s · Runs well (tight fit)

Quick answers

How much memory does FLUX.1 [schnell] need on the NVIDIA RTX PRO 2000 Blackwell Generation Laptop GPU 8GB?

At Q4 GGUF it needs about 5.1 GB. The NVIDIA RTX PRO 2000 Blackwell Generation Laptop GPU 8GB has 8.0 GB usable.

How long does FLUX.1 [schnell] take on the NVIDIA RTX PRO 2000 Blackwell Generation Laptop GPU 8GB?

About 9.9 s per image at Q4 GGUF and default settings, an estimate scaled from a reference timing by this hardware’s compute speed.

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.