Can the RTX 4070 Ti Super 16 GB run HunyuanImage 2.1?
Not at full quality. The 16-bit and 8-bit files are too big for the RTX 4070 Ti Super 16 GB; the largest that fits is Q4_K_M (11.3 GB).
Q4_K_M (11.3 GB) fits with only 1.4 GB to spare (about 14.6 GB of 16 GB). Quality: good; some loss in fine detail and text. For better quality, Q5_K_M (13.4 GB) also runs, with about 0.7 GB spilling into system RAM - a little slower, still practical.
Also try the FP8 file (17.4 GB) on this GPU. It does not fit entirely, but a current ComfyUI streams the overflow from RAM and native 8-bit files often beat a small GGUF that does fit. Dynamic VRAM →
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | hunyuanimage2.1-q4_k_m.gguf Q4_K_M · calcuis/hunyuanimage-gguf | models/unet | 11.3 GB | Download → |
| Text encoder | qwen_2.5_vl_7b_fp8_scaled.safetensors Qwen2.5-VL 7B FP8 · Comfy-Org/HunyuanImage_2.1_ComfyUI | models/text_encoders | 9.4 GB | Download → |
| Text encoder | byt5_small_glyphxl_fp16.safetensors ByT5 small Glyph-XL · Comfy-Org/HunyuanImage_2.1_ComfyUI | models/text_encoders | 0.4 GB | Download → |
| VAE | hunyuan_image_2.1_vae_fp16.safetensors HunyuanImage 2.1 VAE · Comfy-Org/HunyuanImage_2.1_ComfyUI | models/vae | 0.8 GB | Download → |
| Total download · keep about the same free on disk | 21.9 GB | |||
Alternatives: Qwen2.5-VL 7B BF16 (16.6 GB). Sizes from Hugging Face, 2026-10-01.
02Every HunyuanImage 2.1 file on 16 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 34.9 GB | 38.2 GB | 22.2 GB too big | the original weights | Hugging Face → |
| FP8 SAFETENSORS · Comfy-Org | 17.4 GB | 20.7 GB | 4.7 GB too big | practically identical to the original | Hugging Face → |
| Q8_0 GGUF · calcuis | 19.8 GB | 23.1 GB | 7.1 GB too big | practically identical to the original | Hugging Face → |
| Q6_K GGUF · calcuis | 15.7 GB | 19.0 GB | 3.0 GB too big | very close to the original | Hugging Face → |
| Q5_K_M GGUF · calcuis | 13.4 GB | 16.7 GB | spills 0.7 GB | close; small differences in fine detail | Hugging Face → |
| Q4_K_M ← GGUF · calcuis | 11.3 GB | 14.6 GB | fits · 1.4 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · calcuis | 9.8 GB | 13.1 GB | fits · 2.9 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · calcuis | 7.3 GB | 10.6 GB | fits · 5.4 GB spare | heavy loss; a last resort | Hugging Face → |
“Needed” adds 2.5 GB of working memory for HunyuanImage 2.1 and a 0.8 GB reserve. Sizes 2026-10-01.
03Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run HunyuanImage 2.1 on a RTX 4070 Ti Super 16 GB, send your time per image and peak VRAM and it will appear here, credited.
04With more VRAM
With 24 GB you could run HunyuanImage 2.1 at 8-bit or better (FP8, 17.4 GB) with no offloading: for example on the RTX 4090 24 GB, RTX 3090 24 GB, RX 7900 XTX 24 GB.
05About the RTX 4070 Ti Super 16 GB
The RTX 4070 Ti Super 16 GB is an Ada Lovelace GPU with hardware FP8, so ComfyUI can compute Comfy-Org's FP8 files natively: small and fast. (Plain FP8 files use FP8 maths with the --fast fp8_matrix_mult option.)
16 GB GDDR6X · 256-bit · 672 GB/s · Ada Lovelace. Everything that runs on it →
06Questions
How much VRAM does HunyuanImage 2.1 need?
Around 10.6 GB with the smallest file (Q2_K) and 20.7 GB with an 8-bit file (FP8). The full 16-bit file needs about 38.2 GB.
Which HunyuanImage 2.1 file should I download for the RTX 4070 Ti Super 16 GB?
Q4_K_M (11.3 GB) from calcuis/hunyuanimage-gguf. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
FP8 or Q8_0 for HunyuanImage 2.1 on the RTX 4070 Ti Super 16 GB?
FP8 (17.4 GB): the RTX 4070 Ti Super 16 GB computes it natively, while Q8_0 (19.8 GB) is unpacked on the fly.
How much do I need to download for HunyuanImage 2.1 on the RTX 4070 Ti Super 16 GB?
21.9 GB: hunyuanimage2.1-q4_k_m.gguf, qwen_2.5_vl_7b_fp8_scaled.safetensors, byt5_small_glyphxl_fp16.safetensors, hunyuan_image_2.1_vae_fp16.safetensors.