{"slug": "qwen-image-2-1-on-16-gb-of-vram-quantized-to-nf4-a-real-benchmark-against-flux", "title": "Qwen-Image 2.1 on 16 GB of VRAM, quantized to NF4: a real benchmark against FLUX", "summary": "A developer benchmarked Qwen-Image-2.1-Turbo quantized to NF4 against FLUX.1-dev Q6_K with a pixel-art LoRA on a single 16 GB RTX 4070 Ti SUPER, finding Qwen NF4 generated images in 8.4 seconds versus 61 seconds for FLUX at 1344×768 and rendered requested text exactly in 13 of 18 cases versus 7 for FLUX. The writeup also documents failure modes, including an 8-bit LLM.int8 transformer producing a near-uniform purple field and tiled VAE decoding leaving vertical streaks, fixed by decoding the full frame after offloading the transformer and text encoder.", "body_md": "On a 16 GB RTX 4070 Ti SUPER, Qwen-Image-2.1-Turbo does not fit in bf16 (the pipeline is 32.5 GB). Quantized to NF4 with bitsandbytes it does, and I ran it against the FLUX.1-dev Q6_K + pixel-art LoRA that made my blog covers: same prompts, same seeds, same card, every image checked by eye.\n\n**What I measured**\n\n- \n**Speed:** 8.4 s per image for Qwen NF4 (8 steps) against 61 s for FLUX + LoRA (30 steps), median, 1344×768. About 7×.\n- \n**Text in the image:** 13 of 18 requested texts exact for Qwen NF4, 7 for FLUX. Unquantized Qwen on a 48 GB A6000 got 17.\n- \n**Cover prompts:** Qwen NF4 drew the requested scene in 11 of 15 covers; FLUX + LoRA in none (6 almost, 9 missing the main action or object).\n- \n**Memory:** 11.9 GiB over the card's baseline for Qwen NF4 (nvidia-smi), 13.1 GiB for FLUX.\n\n**What broke on the way**\n\n- An 8-bit (LLM.int8) transformer returns a near-uniform purple field. NF4 works.\n- Decoding the VAE in tiles (needed to fit) left faint vertical purple streaks. Fix: make the latents first, drop the transformer and the text encoder from the GPU, then decode the whole frame (0.77 s, 7.1 GiB peak).\n- An automatic reader (gemma3:4b) accepted misspelled text, and an external audit caught five of my own verdicts. Every number in the post comes from the corrected image-by-image review.\n\nWhat I lose: the LoRA's atmosphere. Qwen follows the scene but on a flatter background. I switched anyway: every cover on the blog is now generated by Qwen NF4.\n\n**Read the full measurements, the side-by-side galleries and the code →** [https://efraingaray.com/en/blog/qwen-image-vs-flux/](https://efraingaray.com/en/blog/qwen-image-vs-flux/)\n\nScripts, prompts and the per-image review: [gist](https://gist.github.com/EfrainGaray/427b5cc1875b888fd57e32bf8b886527).", "url": "https://wpnews.pro/news/qwen-image-2-1-on-16-gb-of-vram-quantized-to-nf4-a-real-benchmark-against-flux", "canonical_source": "https://dev.to/efraingaray/qwen-image-21-on-16-gb-of-vram-quantized-to-nf4-a-real-benchmark-against-flux-4jk0", "published_at": "2026-10-10 18:08:42+00:00", "updated_at": "2026-10-10 18:16:14.815983+00:00", "lang": "en", "topics": ["generative-ai", "ai-research", "ai-tools", "machine-learning"], "entities": ["Qwen-Image-2.1-Turbo", "FLUX.1-dev", "bitsandbytes", "RTX 4070 Ti SUPER", "A6000", "gemma3:4b"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/qwen-image-2-1-on-16-gb-of-vram-quantized-to-nf4-a-real-benchmark-against-flux", "markdown": "https://wpnews.pro/news/qwen-image-2-1-on-16-gb-of-vram-quantized-to-nf4-a-real-benchmark-against-flux.md", "text": "https://wpnews.pro/news/qwen-image-2-1-on-16-gb-of-vram-quantized-to-nf4-a-real-benchmark-against-flux.txt", "jsonld": "https://wpnews.pro/news/qwen-image-2-1-on-16-gb-of-vram-quantized-to-nf4-a-real-benchmark-against-flux.jsonld"}}