Short answer for October 2026: start with graphics memory, because it decides which video models you can run at all. A 16GB card is the realistic floor for local clip generation, the 32GB RTX 5090 marks the comfortable consumer ceiling, and 64GB of system RAM plus a 2TB NVMe drive keeps the rest of the machine from becoming the bottleneck.
If you would rather skip the parts list, ready-built options such as the CryptoGPUs AI workstations range come with the GPU, memory and drivers already matched. If you plan to build, the sections below show where the money should go.
Key Takeaways
- Open video models publish very different memory figures, from 14GB with offloading for HunyuanVideo-1.5 to 80GB for full-size Wan2.2 A14B on one GPU.
- Quantized community builds let 12GB and 16GB cards make short clips, but at lower precision and with more waiting.
- NVIDIA’s US list prices start at $379 for the RTX 5060 Ti and $1,999 for the RTX 5090, yet shelf prices in 2026 sit well above those figures.
- System RAM, fast storage and cooling matter more for video than for chatbots, because each job runs for minutes rather than seconds.
How Much VRAM the Current Video and Image Models Ask for
Wan2.2, whose open weights Alibaba’s Wan team released in July 2025, comes in two sizes. The GitHub page says the TI2V-5B model runs on a GPU with at least 24GB, such as an RTX 4090, and produces a 5-second 720P clip at 24 fps in under 9 minutes on a single consumer card. The larger A14B text-to-video and image-to-video models are listed at 80GB for a single GPU unless you turn on offloading.
Tencent’s HunyuanVideo-1.5, an 8.3-billion-parameter model released on November 20, 2025, lists a 14GB minimum with model offloading enabled. Lightricks is more demanding: its July 12, 2026 guide for running LTX-2.5 locally asks for an NVIDIA card with 32GB or more, 32GB of system RAM and 100GB of free disk space, and recommends an 80GB A100 or an H100 for the full setup.
For stills, Black Forest Labs says the 32-billion-parameter FLUX.2 [dev] needs H100-class memory unless you use the quantized versions, while FLUX.2 [klein] 4B, released January 15, 2026, fits in roughly 8GB.
Real users get further than the official minimums suggest. In an r/StableDiffusion thread captured May 8, 2026, one owner of a 12GB RTX 3060 with 32GB of RAM reported 5-second 720p clips from LTX 2.3 in about 140 seconds, or about 95 seconds with the Cache-DiT speedup, using heavily optimized workflows and quantized GGUF files.
GPU Tiers for Creators, With Dated Prices
NVIDIA’s US product pages, checked October 18, 2026, give these starting prices and memory sizes:
- RTX 5060 Ti: from $379, 16GB or 8GB of GDDR7. The $379 figure is the 8GB model; the 16GB version launched at $429 in April 2025. Only the 16GB card makes sense for video models.
- RTX 5070 Ti: from $749, 16GB, with two ninth-generation NVENC encoders for faster exports.
- RTX 5080: from $999, 16GB. It renders faster than the 5070 Ti but holds the same amount of memory, so it does not lift you into a larger model tier.
- RTX 5090: from $1,999, 32GB, with three NVENC encoders and two decoders, the most in the range.
Guru3D reported on September 14, 2026 that the cheapest RTX 5090 listings in Germany had passed 5,000 euros, citing tight GDDR7 supply and demand from AI buyers. Street prices move week to week, so compare a few retailers before you commit; the range of graphics cards at CryptoGPUs, for example, runs from the RTX 5060 Ti 16GB up to the RTX 5090, which makes it easy to line up tiers side by side.
A practical reading: the 16GB cards handle quantized Wan2.2 and HunyuanVideo-1.5 work and most editing jobs, while the 5090 is the only GeForce card that meets Lightricks’ 32GB guidance for LTX-2.5.
System RAM and NVMe Storage for Models and Footage
Offloading moves parts of a model from the graphics card into system memory, so RAM quietly becomes part of your VRAM budget. Lightricks lists 32GB as the minimum and 64GB for its recommended LTX setup. With less, a machine tends to fall back on the page file and slow to a crawl. For a PC that also runs an editor, 64GB is the sensible target, and 128GB pays off if you keep several models loaded.
Storage fills faster than most people expect. The LTX-2 quick start alone downloads roughly 66 GiB, and a handful of video and image checkpoints can pass several hundred gigabytes. Put the operating system and model files on one fast NVMe drive of at least 2TB, keep project footage and caches on a second NVMe drive, and archive finished work to a larger, cheaper disk.
Cooling, Power and Noise During Long Renders
A single video run can hold the GPU near full load for many minutes, then repeat for the next seed. NVIDIA rates the RTX 5060 Ti at 180W with a 600W recommended system supply, the 5070 Ti at 300W and 750W, the 5080 at 360W and 850W, and the 5090 at 575W and 1,000W.
Buy a power supply above those minimums so it runs in its quiet range, choose a case with clear front-to-back airflow and room for a long triple-fan card, and watch GPU temperature during your first long batch. If you record voice-overs in the same room, a larger, slower cooler beats a small, loud one.
Prebuilt Workstation or Self-Build
Building yourself saves money if you already own a good case, power supply and drives, and you only need a new card. A prebuilt makes more sense when you are buying everything at once, want one warranty for the whole machine, or would rather skip driver setup.
If you go prebuilt, compare the parts warranty length, the return window and which operating system and drivers arrive installed. Some AI workstation sellers now ship with Ubuntu 24.04 and CUDA or ROCm already configured, which saves an afternoon of setup.
Whichever route you take, follow this order:
- Pick the largest video model you actually plan to use and note its VRAM figure.
- Choose the GPU tier that meets it, with quantization as your fallback.
- Match system RAM to at least twice the card’s memory.
- Size the power supply above NVIDIA’s recommendation for that card.
- Add two NVMe drives, one for models and one for footage.
Frequently Asked Questions
What is the minimum GPU memory for local AI video models?
16GB is the practical minimum for current open video models with offloading or quantization. 24GB to 32GB lets you run larger models at higher precision with fewer workarounds.
Can an RTX 5060 Ti 16GB run Wan2.2?
Yes, with quantized files and offloading through tools such as ComfyUI or Wan2GP. Expect longer generation times than on a 24GB or 32GB card.
Is the RTX 5080 better than the 5070 Ti for AI video?
It is faster, but both have 16GB, so they run the same models. Unless speed is the priority, spend the difference on RAM.
How much system RAM does an AI video PC need?
Plan on 64GB. Lightricks lists 32GB as the minimum for LTX-2.5 and 64GB in its recommended setup, and offloading leans heavily on system memory.
Should I buy a prebuilt workstation or build my own PC for AI video?
Build if you are reusing good parts and only need a new GPU. Buy prebuilt if you want one warranty, tested cooling and software installed on day one.
The Bottom Line
Spec the machine around the model, not the other way round. Choose VRAM first, then give it 64GB of RAM, two NVMe drives and a power supply with headroom. Check today’s prices against NVIDIA’s list figures, because in 2026 the gap between them can run to thousands of dollars on the top card.w2