Datasheet for local AI49 models98 GPUsData read 2026-09-25
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HunyuanVideo 1.5 VRAM requirements

Tencent's 8.3B video model from late 2025, much lighter than the original 13B HunyuanVideo. The FP8 file is the cfg-distilled variant.

Released 2025-11Licence: Tencent Hunyuan Community License (excludes EU/UK/KR)Steps: 20–50Text encoder: Qwen2.5-VL 7B + glyph encoder (9.4 GB as FP8)
TypeVideo
Parameters8.3B
Fits entirely from10 GB
8-bit or better from13 GB

01The files, and how much VRAM each needs

FileSizeNeededMin. VRAMQualitySource
16-bit16.7 GB21.0 GB21 GBthe original weightsComfy-Org/HunyuanVideo_1.5_repackaged →
Q8_09.0 GB13.3 GB14 GBpractically identical to the originaljayn7/HunyuanVideo-1.5_T2V_720p-GGUF →
FP88.3 GB12.6 GB13 GBpractically identical to the originalComfy-Org/HunyuanVideo_1.5_repackaged →
Q6_K7.0 GB11.3 GB12 GBvery close to the originaljayn7/HunyuanVideo-1.5_T2V_720p-GGUF →
Q5_K_M6.1 GB10.4 GB11 GBclose; small differences in fine detailjayn7/HunyuanVideo-1.5_T2V_720p-GGUF →
Q4_K_M5.1 GB9.4 GB10 GBgood; some loss in fine detail and textjayn7/HunyuanVideo-1.5_T2V_720p-GGUF →

“Needed” = file + 3.5 GB working memory + 0.8 GB system reserve.

03Best GPU for HunyuanVideo 1.5

The cheapest cards (by launch price) that run it well, and every card sorted by memory: best GPU for HunyuanVideo 1.5 → Planning bigger images or longer clips? Open the calculator →

04By graphics card

GPUVRAMVerdictBest fileNeeded
Desktop graphics cards
RTX 2060 6 GB6 GBOffload onlyQ4_K_M9.4 GB
RTX 3050 6 GB6 GBOffload onlyQ4_K_M9.4 GB
RTX 2070 Super 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 2080 Super 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 3050 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 3060 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 3060 Ti 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 3070 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 3070 Ti 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 4060 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 4060 Ti 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 5050 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 5060 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 5060 Ti 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RX 7600 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RX 9050 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RX 9060 XT 8 GB8 GBOffload onlyQ4_K_M9.4 GB
Arc B570 10 GB10 GBRunsQ4_K_M9.4 GB
RTX 3080 10 GB10 GBRunsQ4_K_M9.4 GB
RTX 2080 Ti 11 GB11 GBRunsQ5_K_M10.4 GB
Arc B580 12 GB12 GBRunsQ6_K11.3 GB
RTX 2060 12 GB12 GBRunsQ6_K11.3 GB
RTX 3060 12 GB12 GBRunsQ6_K11.3 GB
RTX 3080 12 GB12 GBRunsQ6_K11.3 GB
RTX 3080 Ti 12 GB12 GBRunsQ6_K11.3 GB
RTX 4070 12 GB12 GBRunsQ6_K11.3 GB
RTX 4070 Super 12 GB12 GBRunsQ6_K11.3 GB
RTX 4070 Ti 12 GB12 GBRunsQ6_K11.3 GB
RTX 5070 12 GB12 GBRunsQ6_K11.3 GB
RX 7700 XT 12 GB12 GBRunsQ6_K11.3 GB
RX 9070 GRE 12 GB12 GBRunsQ6_K11.3 GB
Arc A770 16 GB16 GBRuns wellQ8_013.3 GB
RTX 4060 Ti 16 GB16 GBRuns wellFP812.6 GB
RTX 4070 Ti Super 16 GB16 GBRuns wellFP812.6 GB
RTX 4080 16 GB16 GBRuns wellFP812.6 GB
RTX 4080 Super 16 GB16 GBRuns wellFP812.6 GB
RTX 5060 Ti 16 GB16 GBRuns wellFP812.6 GB
RTX 5070 Ti 16 GB16 GBRuns wellFP812.6 GB
RTX 5080 16 GB16 GBRuns wellFP812.6 GB
RX 7600 XT 16 GB16 GBRuns wellQ8_013.3 GB
RX 7800 XT 16 GB16 GBRuns wellQ8_013.3 GB
RX 7900 GRE 16 GB16 GBRuns wellQ8_013.3 GB
RX 9060 XT 16 GB16 GBRuns wellFP812.6 GB
RX 9070 16 GB16 GBRuns wellFP812.6 GB
RX 9070 XT 16 GB16 GBRuns wellFP812.6 GB
RX 7900 XT 20 GB20 GBRuns wellQ8_013.3 GB
Arc Pro B60 24 GB24 GBRuns well16-bit21.0 GB
RTX 3090 24 GB24 GBRuns well16-bit21.0 GB
RTX 3090 Ti 24 GB24 GBRuns well16-bit21.0 GB
RTX 4090 24 GB24 GBRuns well16-bit21.0 GB
RX 7900 XTX 24 GB24 GBRuns well16-bit21.0 GB
Arc Pro B70 32 GB32 GBRuns well16-bit21.0 GB
RTX 5090 32 GB32 GBRuns well16-bit21.0 GB
Laptop GPUs
RTX 3050 Laptop 4 GB4 GBOffload onlyQ4_K_M9.4 GB
RTX 3050 Ti Laptop 4 GB4 GBOffload onlyQ4_K_M9.4 GB
RTX 2060 Laptop 6 GB6 GBOffload onlyQ4_K_M9.4 GB
RTX 3050 Laptop 6 GB6 GBOffload onlyQ4_K_M9.4 GB
RTX 3060 Laptop 6 GB6 GBOffload onlyQ4_K_M9.4 GB
RTX 4050 Laptop 6 GB6 GBOffload onlyQ4_K_M9.4 GB
RTX 2070 Laptop 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 2070 Super Laptop 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 2080 Laptop 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 2080 Super Laptop 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 3070 Laptop 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 3070 Ti Laptop 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 3080 Laptop 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 4060 Laptop 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 4070 Laptop 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 5050 Laptop 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 5060 Laptop 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 5070 Laptop 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RX 7600M 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RX 7600M XT 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RX 7600S 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RX 7700S 8 GB8 GBOffload onlyQ4_K_M9.4 GB
RTX 4080 Laptop 12 GB12 GBRunsQ6_K11.3 GB
RTX 5070 Laptop 12 GB12 GBRunsQ6_K11.3 GB
RTX 5070 Ti Laptop 12 GB12 GBRunsQ6_K11.3 GB
RX 7800M 12 GB12 GBRunsQ6_K11.3 GB
RTX 3080 Laptop 16 GB16 GBRuns wellQ8_013.3 GB
RTX 3080 Ti Laptop 16 GB16 GBRuns wellQ8_013.3 GB
RTX 4090 Laptop 16 GB16 GBRuns wellFP812.6 GB
RTX 5080 Laptop 16 GB16 GBRuns wellFP812.6 GB
RX 7900M 16 GB16 GBRuns wellQ8_013.3 GB
RTX 5090 Laptop 24 GB24 GBRuns well16-bit21.0 GB
Unified memory
Radeon 8060S (Strix Halo) 96 GB96 GBRuns well16-bit21.0 GB
Apple Silicon Macs (by memory)
Mac 16 GB12.7 GBRunsQ6_K11.3 GB
Mac 18 GB14.4 GBRuns wellQ8_013.3 GB
Mac 24 GB19.6 GBRuns wellQ8_013.3 GB
Mac 32 GB26.8 GBRuns well16-bit21.0 GB
Mac 36 GB30.2 GBRuns well16-bit21.0 GB
Mac 48 GB40.2 GBRuns well16-bit21.0 GB
Mac 64 GB55.7 GBRuns well16-bit21.0 GB
Mac 96 GB85 GBRuns well16-bit21.0 GB
Mac 128 GB115.4 GBRuns well16-bit21.0 GB
Mac 192 GB175.4 GBRuns well16-bit21.0 GB
Mac 256 GB236.9 GBRuns well16-bit21.0 GB
Mac 512 GB498.1 GBRuns well16-bit21.0 GB

On RTX 40/50 GPUs the FP8 file is preferred over Q8_0 when both fit (hardware FP8). All verdicts are calculated; see how the numbers work.

05Text encoder, VAE and other files

FileFolderSizeWhen
Qwen2.5-VL 7B FP8 (scaled)
qwen_2.5_vl_7b_fp8_scaled.safetensors
models/text_encoders9.4 GBtext encoder · smaller, recommendedDownload →
Qwen2.5-VL 7B BF16
qwen_2.5_vl_7b.safetensors
models/text_encoders16.6 GBtext encoder · alternativeDownload →
ByT5 small GlyphXL FP16
byt5_small_glyphxl_fp16.safetensors
models/text_encoders0.4 GBrequiredDownload →
HunyuanVideo 1.5 VAE FP16
hunyuanvideo15_vae_fp16.safetensors
models/vae2.5 GBrequiredDownload →
SigCLIP vision patch14 384
sigclip_vision_patch14_384.safetensors
models/clip_vision0.9 GBoptional · I2V onlyDownload →

The files the official ComfyUI workflows load next to the model. Sizes read from Hugging Face (2026-09-25). Every GPU page for this model lists the exact set to download for that card, with the total.

Qwen2.5-VL 7B + glyph encoder: 16.6 GB as 16-bit, 9.4 GB as FP8. ComfyUI encodes the prompt first and can push the encoder out of VRAM before sampling, so it does not have to fit together with the model. On 16 GB the FP8 encoder fits on its own, so prompt encoding stays fast.

06Where the files go in ComfyUI

FileFolderLoader node
Diffusion model (.safetensors: 16-bit, FP8, INT8)ComfyUI/models/diffusion_modelsLoad Diffusion Model
GGUF file (.gguf)ComfyUI/models/unetUnet Loader (GGUF) — from the ComfyUI-GGUF node pack
Text encoderComfyUI/models/text_encodersLoad CLIP / DualCLIPLoader (or the GGUF versions)
VAEComfyUI/models/vaeLoad VAE

Standard ComfyUI folders. After copying files, press R in ComfyUI (or restart it) to refresh the lists. Some uploads need their uploader's own loader node — see the notes above.

07AMD, Intel and NVIDIA: which file types are fast

File typeRTX 50RTX 40RTX 30 / 20RX 9000RX 7000/6000 · Strix HaloIntel Arc
16-bitRunsRunsRunsRunsRunsRuns
FP8Native FP8Native FP8No FP8 speed-upNative FP8No FP8 speed-upNo FP8 speed-up
GGUFRuns (GGUF node)Runs (GGUF node)Runs (GGUF node)Runs (GGUF node)Runs (GGUF node)Runs (GGUF node)

Every file type loads on every listed GPU, so the memory verdicts apply to all of them. What differs is speed: FP8 maths needs RTX 40/50 or RX 9000 (with ROCm 6.4+ and PyTorch 2.7+); ComfyUI's INT8 maths runs on NVIDIA and AMD, not on Intel; GGUF is unpacked on the fly on any GPU, which costs some speed. NVFP4 files are fast only on RTX 50. AMD runs ComfyUI on Windows through ROCm, Intel through PyTorch XPU; some custom nodes are NVIDIA-only. Source: ComfyUI model_management.py. AMD and Intel guide →

08Measured and reported results

LabelGPUSetupResultPeak VRAMDateSource
reportedRTX 4090 24 GB720p model · 848x480
“The 5 seconds video took 297s to generate so barely longer than on your end”
Replicated the 5090 poster's ComfyUI workflow (720p model at 848x480, 5 s @24fps); card undervolted (~5% slower per poster)
297 s / clip—2025-12-05huggingface.co →
reportedRTX 5090 32 GB720p model · 848x480
“I was able to generate 5 seconds video in 284s at 24fps using the 720p model at 848*480”
ComfyUI; 5 s video at 24 fps; steps not stated
284 s / clip—2025-11-22huggingface.co →

Reported results are other people's numbers, copied as published, with a link. Settings, drivers and ComfyUI versions differ, so compare them with care. Send yours.

09Training a LoRA for HunyuanVideo 1.5

TrainerVRAMTypeSettings and quoteSource
SimpleTuner24 GBstated minimumRank-16 LoRA, full gradient checkpointing, 480p; 720p or larger batches: 48-80GB
“Minimum: 24GB-32GB VRAM is comfortable for a Rank-16 LoRA with full gradient checkpointing at 480p.”
github.com →

reported Figures as stated by each trainer's own documentation or official example configs, read 2026-09-25. “Stated minimum” = the docs call it a minimum; “example run” = a config or measured run at that size. They differ a lot because of settings: an 8-bit or 4-bit base model, block swapping and lower resolution all cut memory. All models →

10Every file tracked for HunyuanVideo 1.5

FileTypeSizeRepo
hunyuanvideo1.5_480p_t2v_cfg_distilled_fp16.safetensors (480p_t2v_cfg_distilled)F1616.65 GBComfy-Org/HunyuanVideo_1.5_repackaged →
hunyuanvideo1.5_720p_t2v_fp16.safetensors (720p_t2v)F1616.65 GBComfy-Org/HunyuanVideo_1.5_repackaged →
hunyuanvideo1.5_480p_t2v_fp16.safetensors (480p_t2v)F1616.65 GBComfy-Org/HunyuanVideo_1.5_repackaged →
hunyuanvideo1.5_720p_i2v_fp16.safetensors (720p_i2v)F1616.65 GBComfy-Org/HunyuanVideo_1.5_repackaged →
hunyuanvideo1.5_480p_t2v_cfg_distilled_fp8_scaled.safetensors (480p_t2v_cfg_distilled)FP88.33 GBComfy-Org/HunyuanVideo_1.5_repackaged →
hunyuanvideo1.5_720p_i2v_cfg_distilled_fp8_scaled.safetensors (720p_i2v_cfg_distilled)FP88.33 GBComfy-Org/HunyuanVideo_1.5_repackaged →
hunyuanvideo1.5_720p_t2v-Q8_0.ggufQ8_09.00 GBjayn7/HunyuanVideo-1.5_T2V_720p-GGUF →
hunyuanvideo1.5_720p_t2v-Q6_K.ggufQ6_K7.02 GBjayn7/HunyuanVideo-1.5_T2V_720p-GGUF →
hunyuanvideo1.5_720p_t2v-Q5_K_M.ggufQ5_K_M6.12 GBjayn7/HunyuanVideo-1.5_T2V_720p-GGUF →
hunyuanvideo1.5_720p_t2v-Q5_K_S.ggufQ5_K_S5.94 GBjayn7/HunyuanVideo-1.5_T2V_720p-GGUF →
hunyuanvideo1.5_720p_t2v-Q4_K_M.ggufQ4_K_M5.09 GBjayn7/HunyuanVideo-1.5_T2V_720p-GGUF →
hunyuanvideo1.5_720p_t2v-Q4_K_S.ggufQ4_K_S4.92 GBjayn7/HunyuanVideo-1.5_T2V_720p-GGUF →

EXTRA (Nov 2025; Comfy-Org repo ~600k downloads). 8.3B DiT. There is no Comfy-Org fp8 of the plain (non-distilled) 720p T2V; fp8_scaled exists for cfg-distilled variants. Also 1080p super-resolution models (hunyuanvideo1.5_1080p_sr_distilled fp16 16662949080 / fp8_scaled 8335262258).