Datasheet for local AI49 models98 GPUsData read 2026-09-25
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ERNIE-Image (Turbo) VRAM requirements

Baidu's 8B model from April 2026, strong at rendering text in images. Turbo runs in about 8 steps; Base takes 50.

Released 2026-04Licence: Apache-2.0Steps: 8 (Turbo)Text encoder: Ministral 3 3B (7.7 GB as 16-bit)
TypeImage
Parameters8.03B
Fits entirely from6 GB
8-bit or better from11 GB

01The files, and how much VRAM each needs

FileSizeNeededMin. VRAMQualitySource
16-bit16.1 GB18.4 GB19 GBthe original weightsComfy-Org/ERNIE-Image →
Q8_08.7 GB11.0 GB11 GBpractically identical to the originalunsloth/ERNIE-Image-Turbo-GGUF →
Q6_K6.8 GB9.1 GB10 GBvery close to the originalunsloth/ERNIE-Image-Turbo-GGUF →
Q5_K_M5.9 GB8.2 GB9 GBclose; small differences in fine detailunsloth/ERNIE-Image-Turbo-GGUF →
Q4_K_M5.0 GB7.3 GB8 GBgood; some loss in fine detail and textunsloth/ERNIE-Image-Turbo-GGUF →
Q3_K_M3.9 GB6.2 GB7 GBnoticeable loss of detailunsloth/ERNIE-Image-Turbo-GGUF →
Q2_K3.2 GB5.5 GB6 GBheavy loss; a last resortunsloth/ERNIE-Image-Turbo-GGUF →

“Needed” = file + 1.5 GB working memory + 0.8 GB system reserve.

03Best GPU for ERNIE-Image

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

04By graphics card

GPUVRAMVerdictBest fileNeeded
Desktop graphics cards
RTX 2060 6 GB6 GBTightQ2_K5.5 GB
RTX 3050 6 GB6 GBTightQ2_K5.5 GB
RTX 2070 Super 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 2080 Super 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 3050 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 3060 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 3060 Ti 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 3070 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 3070 Ti 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 4060 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 4060 Ti 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 5050 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 5060 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 5060 Ti 8 GB8 GBRunsQ4_K_M7.3 GB
RX 7600 8 GB8 GBRunsQ4_K_M7.3 GB
RX 9050 8 GB8 GBRunsQ4_K_M7.3 GB
RX 9060 XT 8 GB8 GBRunsQ4_K_M7.3 GB
Arc B570 10 GB10 GBRunsQ6_K9.1 GB
RTX 3080 10 GB10 GBRunsQ6_K9.1 GB
RTX 2080 Ti 11 GB11 GBRuns wellQ8_011.0 GB
Arc B580 12 GB12 GBRuns wellQ8_011.0 GB
RTX 2060 12 GB12 GBRuns wellQ8_011.0 GB
RTX 3060 12 GB12 GBRuns wellQ8_011.0 GB
RTX 3080 12 GB12 GBRuns wellQ8_011.0 GB
RTX 3080 Ti 12 GB12 GBRuns wellQ8_011.0 GB
RTX 4070 12 GB12 GBRuns wellQ8_011.0 GB
RTX 4070 Super 12 GB12 GBRuns wellQ8_011.0 GB
RTX 4070 Ti 12 GB12 GBRuns wellQ8_011.0 GB
RTX 5070 12 GB12 GBRuns wellQ8_011.0 GB
RX 7700 XT 12 GB12 GBRuns wellQ8_011.0 GB
RX 9070 GRE 12 GB12 GBRuns wellQ8_011.0 GB
Arc A770 16 GB16 GBRuns wellQ8_011.0 GB
RTX 4060 Ti 16 GB16 GBRuns wellQ8_011.0 GB
RTX 4070 Ti Super 16 GB16 GBRuns wellQ8_011.0 GB
RTX 4080 16 GB16 GBRuns wellQ8_011.0 GB
RTX 4080 Super 16 GB16 GBRuns wellQ8_011.0 GB
RTX 5060 Ti 16 GB16 GBRuns wellQ8_011.0 GB
RTX 5070 Ti 16 GB16 GBRuns wellQ8_011.0 GB
RTX 5080 16 GB16 GBRuns wellQ8_011.0 GB
RX 7600 XT 16 GB16 GBRuns wellQ8_011.0 GB
RX 7800 XT 16 GB16 GBRuns wellQ8_011.0 GB
RX 7900 GRE 16 GB16 GBRuns wellQ8_011.0 GB
RX 9060 XT 16 GB16 GBRuns wellQ8_011.0 GB
RX 9070 16 GB16 GBRuns wellQ8_011.0 GB
RX 9070 XT 16 GB16 GBRuns wellQ8_011.0 GB
RX 7900 XT 20 GB20 GBRuns well16-bit18.4 GB
Arc Pro B60 24 GB24 GBRuns well16-bit18.4 GB
RTX 3090 24 GB24 GBRuns well16-bit18.4 GB
RTX 3090 Ti 24 GB24 GBRuns well16-bit18.4 GB
RTX 4090 24 GB24 GBRuns well16-bit18.4 GB
RX 7900 XTX 24 GB24 GBRuns well16-bit18.4 GB
Arc Pro B70 32 GB32 GBRuns well16-bit18.4 GB
RTX 5090 32 GB32 GBRuns well16-bit18.4 GB
Laptop GPUs
RTX 3050 Laptop 4 GB4 GBOffload onlyQ2_K5.5 GB
RTX 3050 Ti Laptop 4 GB4 GBOffload onlyQ2_K5.5 GB
RTX 2060 Laptop 6 GB6 GBTightQ2_K5.5 GB
RTX 3050 Laptop 6 GB6 GBTightQ2_K5.5 GB
RTX 3060 Laptop 6 GB6 GBTightQ2_K5.5 GB
RTX 4050 Laptop 6 GB6 GBTightQ2_K5.5 GB
RTX 2070 Laptop 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 2070 Super Laptop 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 2080 Laptop 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 2080 Super Laptop 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 3070 Laptop 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 3070 Ti Laptop 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 3080 Laptop 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 4060 Laptop 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 4070 Laptop 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 5050 Laptop 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 5060 Laptop 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 5070 Laptop 8 GB8 GBRunsQ4_K_M7.3 GB
RX 7600M 8 GB8 GBRunsQ4_K_M7.3 GB
RX 7600M XT 8 GB8 GBRunsQ4_K_M7.3 GB
RX 7600S 8 GB8 GBRunsQ4_K_M7.3 GB
RX 7700S 8 GB8 GBRunsQ4_K_M7.3 GB
RTX 4080 Laptop 12 GB12 GBRuns wellQ8_011.0 GB
RTX 5070 Laptop 12 GB12 GBRuns wellQ8_011.0 GB
RTX 5070 Ti Laptop 12 GB12 GBRuns wellQ8_011.0 GB
RX 7800M 12 GB12 GBRuns wellQ8_011.0 GB
RTX 3080 Laptop 16 GB16 GBRuns wellQ8_011.0 GB
RTX 3080 Ti Laptop 16 GB16 GBRuns wellQ8_011.0 GB
RTX 4090 Laptop 16 GB16 GBRuns wellQ8_011.0 GB
RTX 5080 Laptop 16 GB16 GBRuns wellQ8_011.0 GB
RX 7900M 16 GB16 GBRuns wellQ8_011.0 GB
RTX 5090 Laptop 24 GB24 GBRuns well16-bit18.4 GB
Unified memory
Radeon 8060S (Strix Halo) 96 GB96 GBRuns well16-bit18.4 GB
Apple Silicon Macs (by memory)
Mac 16 GB12.7 GBRuns wellQ8_011.0 GB
Mac 18 GB14.4 GBRuns wellQ8_011.0 GB
Mac 24 GB19.6 GBRuns well16-bit18.4 GB
Mac 32 GB26.8 GBRuns well16-bit18.4 GB
Mac 36 GB30.2 GBRuns well16-bit18.4 GB
Mac 48 GB40.2 GBRuns well16-bit18.4 GB
Mac 64 GB55.7 GBRuns well16-bit18.4 GB
Mac 96 GB85 GBRuns well16-bit18.4 GB
Mac 128 GB115.4 GBRuns well16-bit18.4 GB
Mac 192 GB175.4 GBRuns well16-bit18.4 GB
Mac 256 GB236.9 GBRuns well16-bit18.4 GB
Mac 512 GB498.1 GBRuns well16-bit18.4 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
Ministral 3 3B
ministral-3-3b.safetensors
models/text_encoders7.7 GBrequiredDownload →
FLUX.2 VAE
flux2-vae.safetensors
models/vae0.3 GBrequiredDownload →
ERNIE prompt enhancer (optional)
ernie-image-prompt-enhancer.safetensors
models/text_encoders6.9 GBoptionalDownload →

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.

Ministral 3 3B: 7.7 GB as 16-bit (an optional prompt-enhancer LLM (6.9 GB) can be skipped). 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 16-bit 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
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

No measured results yet. Send yours.

09Training a LoRA for ERNIE-Image

No trainer documentation with a VRAM figure for this model was found yet. What the trainers say for other models →

10Every file tracked for ERNIE-Image

FileTypeSizeRepo
ernie-image-turbo-int8rowwise.safetensors (turbo)INT88.22 GBBedovyy/ERNIE-Image-Quantized →
needs ComfyUI-INT8-Fast custom node
ernie-image-int8rowwise.safetensors (base)INT88.22 GBBedovyy/ERNIE-Image-Quantized →
needs ComfyUI-INT8-Fast custom node
ernie-image-turbo-fp8.safetensors (turbo)FP88.22 GBBedovyy/ERNIE-Image-Quantized →
fp8 e4m3
ernie-image-fp8.safetensors (base)FP88.22 GBBedovyy/ERNIE-Image-Quantized →
fp8 e4m3
ernie-image-turbo-nvfp4.safetensors (turbo)NVFP44.78 GBBedovyy/ERNIE-Image-Quantized →
ernie-image-nvfp4.safetensors (base)NVFP44.78 GBBedovyy/ERNIE-Image-Quantized →
ernie-image-turbo.safetensors (turbo)16-bit16.07 GBComfy-Org/ERNIE-Image →
dtype not in filename; official weights are BF16
ernie-image.safetensors (base)16-bit16.07 GBComfy-Org/ERNIE-Image →
dtype not in filename; official weights are BF16
ernie-image-turbo-BF16.gguf (turbo)BF1616.07 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-F16.gguf (turbo)F1616.07 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q8_0.gguf (turbo)Q8_08.69 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q6_K.gguf (turbo)Q6_K6.79 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q5_1.gguf (turbo)Q5_16.24 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q5_K_M.gguf (turbo)Q5_K_M5.93 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q5_K_S.gguf (turbo)Q5_K_S5.86 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q5_0.gguf (turbo)Q5_05.75 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q4_1.gguf (turbo)Q4_15.25 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q4_K_M.gguf (turbo)Q4_K_M5.02 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q4_K_S.gguf (turbo)Q4_K_S4.88 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q4_0.gguf (turbo)Q4_04.76 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q3_K_M.gguf (turbo)Q3_K_M3.91 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q3_K_S.gguf (turbo)Q3_K_S3.83 GBunsloth/ERNIE-Image-Turbo-GGUF →
ernie-image-turbo-Q2_K.gguf (turbo)Q2_K3.18 GBunsloth/ERNIE-Image-Turbo-GGUF →

8B DiT from Baidu (HF API 8.03B BF16), strong text rendering. Turbo = 8 steps (DMD+RL distilled), base = 50 steps. VAE: flux2-vae (336213556, FLUX.2 VAE). Prompt enhancer is an optional LLM that rewrites prompts; skip it to save memory. Native ComfyUI support (Comfy-Org repackage).