The 8GB question, answered straight
Can SeedVR2 run on 8GB VRAM? Here is the honest answer
SeedVR2 on 8GB VRAM is a community experiment, not an upstream hardware promise. ByteDance demonstrates H100 usage and the ComfyUI integration maintainer reports testing from roughly 18GB. That does not mean 8GB always fails; it means success depends on quantized models, VAE tiling, small batches, and patience. This page gives you the settings that make an 8GB attempt most likely to work.
This is a SeedVR2.net 8GB explainer based on published ByteDance and ComfyUI guidance. It is not a guarantee that any specific 8GB card will pass. The hosted tool runs without a local GPU, so it is a practical fallback when an 8GB attempt stalls.
A hosted test sidesteps the 8GB wall
When an 8GB local run keeps failing, upload one short lawful source and read the credit preflight. You still learn whether SeedVR2 helps your file without spending another evening on memory tuning.
First eligible 5-second 720p Standard run: free
Verify your email to use your first video run without credits or a card. A failed run does not use the trial; longer clips and other settings show their exact credit cost before submission.
Browser upload limit: 500 MB. For larger files, paste a publicly accessible HTTPS URL. Each job supports videos up to 10 minutes.
Quick scene presets
Presets pick a model for a common scenario. You can still switch models manually below.
Upscaled preview
Compare the enhanced output before exporting the final file.
Upload a clip, paste a direct video URL, or load the sample. No example video is downloaded until you ask for it.
Tip: keep your original FPS and avoid pre-scaling. The upscaler handles cadence better than most NLE plugins.
Test before processing the full video
Upscaling cannot recreate detail removed by heavy messaging compression. Test the hardest 5–10-second segment first; a higher-priced model is not automatically a better fit.
The 8GB settings that matter
These values come from the published SeedVR2 guidance for the lowest VRAM tier. Treat them as a starting point to test on your own card.
- Model
- 3B or GGUF Q4_K_M/Q8_0
- Target
- 720p–1080p first
- VAE tiling
- On when encode fails
- Official status
- Not supported
Start with the smallest model; quantized GGUF only when needed.
Then 2K stills; avoid long 4K video on the first try.
Start near 1024 tile and 128 overlap when decode hits OOM.
Upstream documents H100; the maintainer reports roughly 18GB in testing.
The straight answer on 8GB
There is no upstream promise that SeedVR2 runs on 8GB. ByteDance publishes H100 reference commands, and the ComfyUI integration maintainer documents testing from about 18GB VRAM. Anyone claiming a guaranteed 8GB path is overstating what the published material says.
That said, people do run smaller SeedVR2 workloads below the documented floor using community quantization, VAE tiling, CPU offload, and BlockSwap. It is a measured experiment, not a product feature, and a successful launch is not a quality or delivery guarantee.
- No official 8GB floor exists.
- 18GB is the documented testing point, not a hard minimum.
- Treat 8GB success as an experiment you must reproduce.
Settings for an 8GB attempt
Start with the native 3B INT8 model and one small source at 720p or 1080p. If the model itself will not fit, move to a GGUF quantization such as Q4_K_M or Q8_0. If the log shows encode or decode pressure, enable VAE tiling near a 1024 tile with 128 overlap.
Keep the video batch small but useful: image batch one, video batch five for motion consistency. BlockSwap is the last lever, and only after a repeatable CUDA OOM proves the model cannot fit even quantized.
Change settings in this order
Resolution first, batch second, quantization third, VAE tiling fourth, BlockSwap last. Reversing this order is the most common way an 8GB session becomes an all-night tuning loop with nothing to show.
Record every value that produced a working run. On 8GB the margin is thin enough that a single forgotten flag can turn a passing run into a failing one after an update.
- Resolution, then batch.
- Quantization, then VAE tiling.
- BlockSwap only after a repeatable OOM.
What an 8GB run costs you
Smaller tiles and more swapping add seams and sharply increase runtime. A one-minute job on a large card can become a twenty-minute job on 8GB, and the output can still carry tiling seams if the overlap is too low.
Count that time against the value of the asset. If a client file must ship tonight, the hosted workspace is usually the cheaper path; keep the 8GB experiment for when you want local ownership, not for when a deadline is burning.
When to stop and go hosted
Stop the 8GB experiment after a bounded sequence of changes. If the model still will not produce a clean small pass, switch to the browser and finish the file. Local debugging can continue later without blocking delivery.
A hosted test also answers the question most people are actually asking: does SeedVR2 improve my media? That answer is worth a credit cost, and it does not depend on your GPU.
8GB guidance limits
Memory behavior varies by card, driver, clip length, and graph. These settings are published starting points, not a promise that a specific 8GB card will pass. Validate on your own hardware with one small source.
- An 8GB card with 8GB usable under load behaves differently from an empty one.
- GGUF and FP8 are community or integration choices, not ByteDance releases.
- A successful 8GB launch is not a quality or temporal-consistency guarantee.
SeedVR2 8GB VRAM questions
Can SeedVR2 run on 8GB VRAM?
It is not officially supported. ByteDance documents H100 usage and the ComfyUI maintainer reports testing from roughly 18GB. Smaller workloads can sometimes run on 8GB through community quantization, VAE tiling, and BlockSwap, but success is an experiment, not a guarantee.
What settings should I use on an 8GB card?
Start with the 3B INT8 model, one small source, and 720p or 1080p. Then try a GGUF Q4_K_M or Q8_0 quantization, enable VAE tiling near 1024/128 when decode fails, and reserve BlockSwap for a repeatable OOM.
What batch size is safe on 8GB?
Use batch one for still images and batch five for video when motion consistency matters. Do not drop video batches below the useful range before trying memory optimizations, because tiny batches can reduce temporal stability.
Is it worth running SeedVR2 locally on 8GB?
Only if you need local ownership or a repeatable offline graph. If the goal is one finished file, a hosted test is usually faster and cheaper than the 8GB tuning loop, and it answers the same question about your media.