Pick your hardware. Get straight answers — no lab-benchmark optimism, just what actually runs, based on the machines we use to publish AI videos every single day.
The hardware behind these verdicts
Our honest picks per budget — the exact tiers the calculator is scoring. (Amazon affiliate links — disclosed, no extra cost to you.)
| Tier | What to get | Why |
|---|---|---|
| Entry ($300-ish) | RTX 4060 8GB | The 8 GB floor where SDXL images, 8-9B chat, and real lip-sync all start working |
| Sweet spot ($550-ish) | RTX 5070 12GB | 12 GB = fast SDXL, 14B chat, and the realistic floor for local video |
| Serious ($1000-ish) | RTX 5080 16GB | 16 GB runs big modern models with LoRA stacks, fast |
| Everything | RTX 5090 32GB | Our reference card — the whole studio at once |
| Don’t skimp | 64GB DDR5 kit · 2TB NVMe SSD | Models live in RAM when VRAM runs out; checkpoints eat disk for breakfast |
One honest note: a used previous-gen card with more VRAM beats a new card with less, every time. VRAM is the currency here.
Can I run AI locally without a graphics card?
Yes — small chat models (3–4B parameters) and even voice cloning run on an ordinary CPU with 16 GB of RAM; they’re slower than GPU models but completely real and completely private.
- Works on CPU: chat via Ollama or LM Studio, lightweight voice cloning, transcription with Whisper
- Needs a GPU: image generation, video, real-time lip-sync
- No GPU yet? Rent one by the hour on RunPod (referral link — new accounts get a signup credit bonus) — a few dollars buys an afternoon on a big-VRAM card, which is the cheapest way to find out what you actually need before buying hardware
How much VRAM do I need to run a local ChatGPT alternative?
8 GB of VRAM runs an 8–9B parameter model comfortably — the tier where local chat stops feeling like a toy — and 12–16 GB runs the models most people can’t tell apart from cloud chatbots.
- 4–6 GB → 3–7B models: capable helpers, visible limits
- 8–12 GB → 8–14B: the everyday sweet spot
- 16–24 GB → 14–32B: genuinely excellent
- Rule of thumb: a Q4-quantized model needs roughly 0.6–0.7 GB of VRAM per billion parameters, plus 1–2 GB for context
What GPU do I need for a talking AI avatar?
Real-time lip-sync starts working at about 8 GB of VRAM; a full companion — voice, brain, and animated face running at the same time — wants 12 GB or more.
The full walkthrough is our avatar overview, then lip-sync and a voice of your own. Total software cost: zero.
Is 12 GB of VRAM enough for Stable Diffusion and local video?
Yes — 12 GB runs SDXL-class image generation fast and sits right at the realistic floor for local video generation (LTX-class models).
Start with ComfyUI from zero, then video in the graph. 8 GB can generate images happily; video is where 12–16 GB earns its keep.
What can a laptop with 6 GB VRAM actually do?
More than you’d think: 7B chat models, good local text-to-speech, SD 1.5 image generation, and Whisper transcription — the whole beginner path except video.
Numbers move as models improve — we update this page from our own bench. Want the full builds these verdicts come from? Start at the beginner path or jump straight to the companion.