The companion episode — the full rig guide, with every board animated.
Almost everyone shopping for an AI computer asks the same first question: how fast is it? Wrong question. The number that decides whether you can run something at all — before speed ever enters the conversation — is memory. Video memory. VRAM.
This guide is the one-page version of the episode: what actually matters, what to buy at every budget in a market that has frankly lost its mind, and when you shouldn’t buy at all.
Disclosure: product links below are affiliate links (Amazon Associate / referral) — they cost you nothing and help keep every guide free. We only recommend what we’d buy ourselves. Full disclosure.
The one number that matters
A model is billions of numbers, and all of them have to live in your graphics card’s memory while it runs. Fits in VRAM → it flies. Spills into system RAM → everything crawls. So think of VRAM as the size of your AI’s desk: a bigger desk doesn’t make you faster — it makes bigger jobs possible.
Two jobs happen when an AI answers you: reading your prompt (raw compute) and writing the answer word-by-word (memory bandwidth — this is why two cards with identical “power” can feel completely different).
The rule of thumb: at everyday (Q4) quality, a model needs a little over half a gigabyte of VRAM per billion parameters.
| Model size | VRAM needed | Runs on |
|---|---|---|
| 8B | ~5 GB | almost any modern card |
| 32B (the local sweet spot) | ~20 GB | used 3090 / 4090 / 5090 |
| 70B | ~40 GB+ | dual GPUs, Mac 128GB, or an AI appliance |
The 2026 memory crisis (read this before buying anything)
The AI datacenter buildout has drained the world’s memory-chip supply, and GPU street prices have gone haywire. The RTX 5090 lists at $1,999 — on the street it’s routinely $3,000–$4,300+, when you can find one. People call them paper cards: the price exists, the card doesn’t.
In this market the smart moves are: buy the value (the cards still near MSRP), buy used (24GB of yesterday’s flagship), or don’t buy — rent (more below).
The GPU board — sorted by memory
| Card | VRAM | Street (Jul 2026) | The take |
|---|---|---|---|
| RTX 5090 | 32 GB | ~$3,000–4,300 | the king — and the crisis poster child |
| RTX 5080 | 16 GB | ~$1,500 | caught in the price mess |
| RTX 5070 Ti | 16 GB | ~$919–1,200 | the sane Blackwell 16GB |
| RX 9070 XT ★ | 16 GB | ~$650 (≈MSRP!) | the value pick — ROCm 7.2 runs natively on Windows, out-of-the-box Ollama/LM Studio/ComfyUI |
| RTX 3090 (used/renewed) ★ | 24 GB | used market | the smart money — max VRAM per dollar |
| RTX 4060 Ti 16GB | 16 GB | ~$500s | quietly beats faster 8GB cards |
| Arc B580 | 12 GB | ~$303 | the entry ticket |
Apple silicon is the other road: unified memory means a Mac can hold 128–256 GB — models no single gaming card can touch — but it generates at a comfortable reading pace, not a blur. Buy a Mac to hold one huge model quietly; buy NVIDIA/AMD to iterate fast.
The AI appliance — the new category
Instead of building a PC around a GPU, you can now buy a finished AI box: NVIDIA DGX Spark ($3,999, 4TB) or its cheaper twin ASUS Ascent GX10 ($2,999, 1TB) — the same GB10 Grace Blackwell chip, 128 GB of unified memory in something you hold in one hand.
The honest trade: that memory is slower than a GPU’s, so it generates at reading pace. You don’t buy one for fast video — you buy it to hold giant 70–120B models and run always-on agent teams, quietly, sipping power. You can even stack two. (It’s on our own wishlist.)
Match the rig to the dream
| You want to… | Needs | That means |
|---|---|---|
| Chat, write, code | 8–12 GB | any modern 12GB card |
| The genuinely smart 32B models | ~20 GB | used 3090, or 5090 headroom |
| Make images (SDXL → FLUX) | 12–24 GB | 12GB entry, 24GB for the best |
| Make video 🐉 | 16–24+ GB | the hungriest thing on this channel |
Chat is light. Pictures are medium. Video is the dragon. Buy for the heaviest thing you’ll actually do — not the heaviest thing that exists.
The four builds
| Build | GPU | RAM | ~Total | Runs |
|---|---|---|---|---|
| Starter | 16GB (4060 Ti / 9070 XT) | 32 GB | ~$900–1,000 | mid models, images, audio |
| Creator | more VRAM | 64 GB | ~$1,500 | smooth images, entry video |
| Pro | 24GB (used 3090 / 4090) | 64–96 GB | ~$2,000–3,000 | 70B models, full-quality video |
| Dream | dual high-end | 128 GB | $4,000+ | agent fleets — you don’t need this |
Most people should stop at Starter — and that’s correct. Prefer a prebuilt? Our own rig is an iBuyPower — links on the start-here guide.
When the model doesn’t fit
- Make it fit anyway — offloading keeps part of the model on the GPU and parks the rest in system RAM. Slower, but it runs. This is why a 64GB RAM kit is the best quiet upgrade in local AI.
- Rent it — RunPod (referral — new accounts get a one-time credit bonus) spins up a real RTX 5090 for ~$0.99/hr, billed by the second. Perfect for trying before buying, weak PCs, or keeping your GPU free for gaming.
Buy vs rent — the honest math
Renting a 5090 ≈ $1/hr. Buying one at street price ≈ $3,000. That’s roughly 3,000 hours (~126 days of nonstop use) to break even — before electricity.
- Buy if you’ll run it daily for months and you value privacy and never seeing a meter. It’s yours forever — that’s the whole ethos here.
- Rent if you’re starting out, unsure, on a weak PC, or the card you want is literally out of stock. Right now, rent-first is genuinely smart money.
Prices verified July 2026 — this page gets updated as the market settles. Start building at askaillex.com, and bring questions to r/aillex.