VRAM & Hardware Guide for AI Video Generation (2026)

⚡ Quick Answer

For local AI video generation in 2026, you need a minimum of 8GB VRAM (LTX-2.3, GGUF-quantized) to get anything running at all, 16GB to run all three major models — LTX-2.3, WAN 2.2, and HunyuanVideo 1.5 — comfortably, and 24GB to skip quantization tricks entirely. VRAM, not GPU speed, is what decides which models you can even load.

Getting an out-of-memory crash on your third attempt at a WAN 2.2 render is usually a VRAM problem, not a settings problem — and knowing how much VRAM you actually need before you buy a GPU or download a 20GB model file saves both money and disk space. This guide covers exact VRAM tiers for LTX-2.3, WAN 2.2, and HunyuanVideo 1.5, which GPU to buy at each budget in 2026's tight GPU market, and what to do if you don't have a compatible Nvidia card at all.

If you haven't compared the three current models yet, start with Best Local AI Video Model 2026 — this guide picks up where that one leaves off, going deeper into the hardware side.

Check Your VRAM Before You Start

Tested on: ComfyUI v0.7.0, RTX 4090 (24GB), RTX 4080 (16GB) | Minimum VRAM: 8GB (LTX-2.3 GGUF only)

Before downloading anything, check two numbers, not one. VRAM (memory built into your graphics card) is a hard ceiling — if a model needs more VRAM than your card has, ComfyUI cannot load it, no matter how fast your GPU is otherwise. System RAM is separate, used for offloading — 16GB minimum, 32GB recommended if any model needs CPU offloading.

On Windows, open Task Manager > Performance > GPU. Your VRAM is listed under Dedicated GPU memory.
On Linux or via terminal, run nvidia-smi. The VRAM total appears in the top-right of the output table.

Windows Task Manager Performance tab showing the GPU's Dedicated GPU memory figure — Task Manager's Performance > GPU tab — only the 'Dedicated GPU memory' number is real VRAM.

Tip: Don't confuse "Shared GPU memory" (borrowed from system RAM) with "Dedicated GPU memory" in Task Manager. Only the dedicated number is real VRAM.

How Much VRAM Do You Actually Need?

The answer changes depending on which of the three current local video models you're running, and whether you're doing text-to-video (T2V) or image-to-video (I2V).

VRAM	LTX-2.3	WAN 2.2	HunyuanVideo 1.5
8GB	✅ GGUF quantized only	❌ Not usable	❌ Not usable
12GB	✅ FP8 distilled	⚠️ Usable with block swap, slow	❌ Not usable
16GB	✅ Comfortable	✅ 14B FP8 with block swap	⚠️ Usable with offloading
24GB	✅ Full precision	✅ Comfortable, no offload	✅ Comfortable

HunyuanVideo 1.5 has the highest floor of the three — its 8.3B parameters need roughly 14GB just to load with offloading enabled, and 24GB to run without tricks. LTX-2.3 has the lowest floor thanks to its GGUF-quantized build, which is why it's the only one of the three that runs on an 8GB card at all.

Warning: Downloading the full BF16 or FP16 version of any of these three models "to be safe" under 16GB VRAM will not load. You'll waste 20–40GB of download and disk space for a file your GPU can't run. Always check the tier table above before downloading.

Which GPU Should You Buy in 2026?

GPU pricing in 2026 is unusually distorted — AI data centers are absorbing most of the world's GDDR7 memory supply, which has pushed gaming-card prices well above their original MSRPs. Budget with that in mind rather than the MSRP printed on the box.

8–12GB tier — entry point

The RTX 5060 Ti 16GB (~$429 MSRP, often $480–530 at retail) is the safer buy over its 8GB sibling — always take the 16GB variant if choosing between the two. This tier limits you to LTX-2.3 only.

16GB tier — the sweet spot for AI video

The RTX 5070 Ti (16GB GDDR7, ~$749 MSRP, commonly $820–950 at retail in mid-2026) unlocks all three models with quantization. On the AMD side, the RX 9070 XT (16GB GDDR6) sits at roughly $630–700 and is a legitimate alternative now that ComfyUI Desktop has official Windows ROCm support.

24GB tier — no compromises

An RTX 4090 (used/refurbished, ~$2,499–2,799) or RTX 5090 (32GB, MSRP $1,999 but frequently $2,900–4,300+ at retail due to the ongoing VRAM shortage) runs all three models at full precision with no offloading. A used RTX 4090 gives you the same 24GB ceiling most video workflows actually need, for less than a new 5090.

Tip: Don't chase the RTX 5090 purely for AI video. Its 32GB only matters if you're running full-precision HunyuanVideo 1.5 or stacking multiple models in VRAM at once — a 24GB card covers nearly everything in this guide.

Running AI Video Generation on 8GB VRAM

Quantization, in plain terms, is a compressed version of a model file — it trades a small amount of quality for a much smaller file size and lower VRAM use. Two formats matter here:

GGUF splits the model into chunks and loads only what's needed at once — LTX-2.3's GGUF build is what makes 8GB VRAM usable at all. FP8 stores the model's numbers in a lower-precision format; it needs slightly more VRAM than GGUF but usually runs faster.

A distilled model is a smaller, faster version of the same model, trained to approximate the full model's output in fewer steps. LTX-2.3's distilled pipeline is what gets a 5-second clip down to 20–40 seconds on higher-VRAM cards, and it's also what makes 8GB generation tolerable rather than unusably slow.

Download the GGUF build of LTX-2.3, not the FP8 or full-precision version.
In ComfyUI, load it through a GGUF loader node (installed automatically by ComfyUI Manager if missing) rather than the standard checkpoint loader.
Start at 512 resolution and 49 frames before increasing either setting. Confirm a successful generation at this floor first.
Increase resolution one step at a time — 512 → 720 → 1080 — checking VRAM headroom after each jump.

Tip: For WAN 2.2 specifically, the community Lightning LoRA (a distilled step-reduction LoRA) cuts a 40–50 minute full render down to 1–3 minutes at 4 steps, and it also lowers peak VRAM use — worth adding even above 16GB.

Fixing "CUDA Out of Memory" During Generation

"CUDA out of memory" or "Allocation on device" in the KSampler node

Your VRAM doesn't match the model variant, resolution, or frame count you set.

ComfyUI KSampler node showing a red 'Allocation on device' out of memory error — The KSampler node's red error box — this exact text means your VRAM ran out mid-generation.

Confirm you downloaded the quantized (GGUF or FP8) version, not full BF16/FP16, if you're under 16GB.
Lower resolution or frame count before switching to a different model entirely — this is usually the actual fix.
In ComfyUI Settings > Server config, set VRAM management mode to lowvram. This splits the model into parts to use less memory per step.
Close other GPU-heavy applications (browsers with hardware acceleration, other generation tools) and retry.

Generation runs but is extremely slow, not crashing

This usually means ComfyUI silently fell back to CPU offloading because your VRAM is nearly full, not that anything is broken.

Check Task Manager's GPU usage during generation — if it's near 0%, offloading is active.
Free VRAM by closing background apps, then retry.
If it's still offloading, your model choice is above your card's comfortable tier — drop to a lower-VRAM quantization.

No Nvidia GPU? AMD and Apple Silicon Options

You're not locked out of local AI video without an Nvidia card, though support is newer and less mature.

AMD (Windows)

As of January 2026, ComfyUI Desktop added official AMD ROCm support starting with v0.7.0 — Windows users no longer need WSL or a Linux dual-boot to get real GPU acceleration. During installation, ROCm should be automatically selected if you have a supported Radeon GPU or Ryzen AI processor.

The ComfyUI Desktop installer with AMD ROCm automatically selected during setup — The ComfyUI Desktop installer auto-selecting AMD ROCm on a supported Radeon GPU.

Warning: ROCm on Windows is still newer than CUDA. Some users report workflow crashes on a clean install — if you hit this, installing the specific ROCm 7.1.1 or 7.2.1 preview driver version (rather than the newest available) resolves most cases.

Apple Silicon (Mac)

ComfyUI runs natively on M1/M2/M3 Macs via Metal acceleration. Video generation is significantly slower than an equivalent Nvidia setup, and unified memory takes the place of dedicated VRAM, so an M-series Mac with 32GB+ unified memory is the realistic floor for video work. See the dedicated ComfyUI Mac installation guide for setup steps.

No Good GPU? Renting a Cloud GPU

If your hardware doesn't clear the 16GB tier and buying isn't an option right now, renting is a legitimate middle ground for occasional generation.

Provider (RTX 4090, 24GB)	Price	Notes
RunPod — Community Cloud	~$0.34–0.39/hr	Cheapest tier, third-party hosts, more variable uptime
RunPod — Secure Cloud	~$0.69/hr	Vetted datacenters, roughly double the price
Vast.ai	Often slightly cheaper	Marketplace-based, more setup effort, best if renting often

Both platforms offer one-click ComfyUI templates, so you're not configuring CUDA drivers manually.

Warning: Billing runs per second from pod start to termination on most platforms — a 4090 left running 24/7 costs roughly $250/month even at the cheap Community Cloud rate. Stop or destroy the pod when you're done, not just close the browser tab.

More Troubleshooting

Black frames or no video output

Almost always a missing or misplaced text encoder file rather than a VRAM issue. Each model needs its text encoder(s) in models/text_encoders/ exactly as named in its dedicated setup guide — check the filename, not just the folder.

For anything not covered here, see the full ComfyUI troubleshooting guide.

Frequently Asked Questions

8GB gets you LTX-2.3 only, using its GGUF build. 16GB runs all three current models — LTX-2.3, WAN 2.2, and HunyuanVideo 1.5 — with quantization. 24GB runs all three without offloading tricks.

Only for LTX-2.3's GGUF-quantized build, starting at low resolution and frame count. WAN 2.2 and HunyuanVideo 1.5 both need significantly more VRAM to load at all.

Yes, on Windows, since ComfyUI Desktop added official AMD ROCm support in January 2026 (v0.7.0). It is newer than Nvidia CUDA support and occasionally needs a specific driver version to avoid crashes, but it no longer requires WSL or Linux.

Yes, via Metal acceleration on Apple Silicon (M1/M2/M3), though it is slower than an equivalent Nvidia GPU. 32GB or more unified memory is the realistic floor for video workflows rather than image-only generation.

GGUF splits a model into chunks and loads only what is needed, giving the lowest VRAM floor. FP8 stores weights in a lower-precision format, needing slightly more VRAM than GGUF but usually generating faster.

For occasional use, yes — an RTX 4090 rents for roughly $0.34 to $0.69 per hour on RunPod, so dozens of hours of generation cost less than a fraction of the card's purchase price. For daily heavy use, owning a GPU becomes cheaper within a few months.

What to Do Next

Check your VRAM, then download the right build

Check your VRAM using the Task Manager or nvidia-smi method above, match it against the tier table, then download the correctly quantized build for your GPU — not the full-precision version. Once you have one successful generation, move on to the roadmap to pick which model to specialize in next.

Compare the Models →ComfyUI Roadmap →

Published: 2026-07-19 · Last updated: 2026-07-19 · Models covered: LTX-2.3 (Lightricks), WAN 2.2 (Alibaba), HunyuanVideo 1.5 (Tencent)