Short answer: for running AI models on your own computer, VRAM (the graphics card’s memory) matters more than anything else. A card can only run a model that fits in its memory. In 2026 the practical picks are:
- 16GB: an RTX 5060 Ti 16GB to start.
- 24GB: a used RTX 3090 for the most memory per dollar.
- 32GB: an Intel Arc Pro B70 or AMD Radeon AI PRO R9700 if you want 32GB without paying RTX 5090 prices.
- 64–128GB: a unified-memory computer (AMD Ryzen AI Max+, NVIDIA DGX Spark or Mac Studio) for the biggest models.
Buying a computer mainly for video editing? Our best computer for video editing guide has different priorities.
Quick picks: graphics cards and systems for AI
| Memory tier | Pick | Memory | Price | Largest model, comfortably* |
|---|---|---|---|---|
| Start here | NVIDIA GeForce RTX 5060 Ti 16GB | 16GB | $429 MSRP · ~$780–$830 street | About 14B |
| Cheapest 16GB | AMD Radeon RX 9060 XT 16GB | 16GB | ~$530 street | About 14B |
| Best value | Used NVIDIA GeForce RTX 3090 | 24GB | ~$1,700–$2,000 asking | About 32B |
| 32GB on a budget | Intel Arc Pro B70 | 32GB | $949 MSRP | About 32B, longer context |
| 32GB on a budget | AMD Radeon AI PRO R9700 | 32GB | ~$1,299 listed | About 32B, longer context |
| Fastest single card | NVIDIA GeForce RTX 5090 | 32GB | $1,999 MSRP · $6,500+ street | About 32B, very fast |
| Biggest models | AMD Ryzen AI Max+ 395 systems | 128GB shared | Was $2,566 (Framework Desktop, Dec 2025) | About 120B |
| Biggest models | NVIDIA DGX Spark | 64 or 128GB shared | From $4,999 (64GB) · $6,950 (128GB) | Up to ~100B (64GB) |
| Professional | NVIDIA RTX PRO 6000 Blackwell | 96GB | ~$8,000 launch · $9,000+ now | 70B at high quality |
*At the common 4-bit (Q4) compression, with room for a normal-length conversation. Prices are launch prices or prices reported in October 2026. They are moving fast, so check before you buy.
Why VRAM comes first for AI
When you run an AI model locally, with tools like Ollama, LM Studio or llama.cpp, the whole model has to sit in memory. If it doesn’t fit, it either won’t load or it spills into much slower system RAM and crawls.
Most people run models “quantized” to 4 bits (Q4), which shrinks them with little loss in quality. At Q4, the model weights alone need about:
| Model size | Weights at Q4 | With ~20% for context | Fits on |
|---|---|---|---|
| 8B (e.g. Llama 3.1 8B) | ~5GB | ~6GB | Any 8GB+ card |
| 14B | ~8GB | ~10GB | 12–16GB cards |
| 32B (e.g. Qwen2.5 32B) | ~20GB | ~24GB | 24GB cards (tight) or 32GB cards |
| 70B (e.g. Llama 3.3 70B) | ~40GB | ~48GB | 96GB card, two 24GB+ cards, or 64GB+ unified memory |
Longer conversations and bigger documents need more memory than this.
How fast are they?
Speed is measured in tokens per second, roughly words per second. These figures come from computingforgeeks’ October 2026 tests in Ollama at Q4:
| Card | 8B model | 32B model | 70B model |
|---|---|---|---|
| RTX 3090 (24GB) | 119 tok/s | 37 tok/s | Does not fit |
| RTX 4090 (24GB) | 148 tok/s | 43 tok/s | Does not fit |
| RTX 5090 (32GB) | 250 tok/s | 71 tok/s | Does not fit |
Anything above about 20 tokens per second feels fluid to read. All three cards are fast enough for 32B models. The limit is memory, not speed.
The best graphics cards for AI in 2026
1. Start here: NVIDIA GeForce RTX 5060 Ti 16GB
Launch price: $429 · Reported street price: about $780–$830
The cheapest current NVIDIA card with 16GB. NVIDIA matters because most AI software, especially image and video generation tools, is built and tested on NVIDIA’s CUDA platform first. It runs 8B and 14B chat models well and draws only about 180W. Buy the 16GB version, not the 8GB one.
Cheaper alternative: the AMD Radeon RX 9060 XT 16GB (about $530) works well in Ollama and llama.cpp. Expect more setup friction with tools that assume NVIDIA.
Check RTX 5060 Ti 16GB price on Amazon
2. Best value: used NVIDIA GeForce RTX 3090 (24GB)
Reported asking price: about $1,700–$2,000 (completed sales often lower)
Still the cheapest way to get 24GB on NVIDIA, which is enough for 32B models. It is a 2020 card, so buy with care:
- Buy from a seller with returns.
- Check that it has never been used for crypto mining.
- Make sure your power supply can handle a 350W card.
Prices have roughly doubled in two years because of the memory shortage.
Check renewed RTX 3090 listings on Amazon
3. 32GB without the RTX 5090 price: Intel Arc Pro B70 or AMD Radeon AI PRO R9700
Intel Arc Pro B70: 32GB, 608 GB/s memory bandwidth, $949 launch price (March 2026).
AMD Radeon AI PRO R9700: 32GB, about $1,299 listed, though supply has been patchy.
Both give you 32GB, which means room for 32B models with long conversations, at a fraction of an RTX 5090’s street price. The trade-off is software: llama.cpp and Ollama-style tools work, but many tutorials and image-generation tools assume NVIDIA. Pick one of these if you mostly run chat models and are comfortable troubleshooting.
Check Arc Pro B70 on Amazon Check Radeon AI PRO R9700 on Amazon
4. Fastest single card: NVIDIA GeForce RTX 5090 (32GB)
Launch price: $1,999 · Reported street price: $6,500 and up
The fastest consumer card for AI by a wide margin: about twice the RTX 3090 in the tests above. It still can’t fit a 70B model at Q4 on its own. At current prices, two used RTX 3090s (48GB total) or a 128GB Ryzen AI Max+ system cost less and hold bigger models. We’d only buy one near launch price.
Check RTX 5090 price on Amazon
Want to run 70B+ models? Look at unified-memory computers
Unified-memory machines share one large pool of memory between the CPU and graphics. They are slower per token than an RTX 5090, but they can hold models no single consumer card can.
AMD Ryzen AI Max+ 395 mini PCs (up to 128GB)
AMD says a 128GB system can run its GPT-OSS 120B model locally. In AMD’s own comparison, the Framework Desktop 128GB delivered about 1.7× more tokens per dollar than a DGX Spark. The Framework was $2,566 in December 2025; memory prices have risen since. This is the best-value way into very large models.
See Framework Desktop (not an affiliate link)
NVIDIA DGX Spark (64GB or 128GB)
A small AI computer with NVIDIA’s full software stack built in. NVIDIA says the 64GB model runs models up to about 100 billion parameters.
- 64GB: from $4,999, through Acer, ASUS, Dell and others, from October 23, 2026.
- 128GB Founders Edition: $6,950. It launched at $3,999 in 2025.
Choose it if you want NVIDIA compatibility and don’t mind paying for it.
Apple Mac Studio (M5 Max or M5 Ultra)
The M5 Ultra starts at $5,499 with 96GB of memory and very high memory bandwidth (about 1.2 TB/s). Ollama and LM Studio support Macs well. The M5 Max starts at $2,499. It’s a good fit if you already work on a Mac, though NVIDIA cards are generally the easier path for image generation.
Professional: NVIDIA RTX PRO 6000 Blackwell (96GB)
One card with 96GB, enough to run 70B models at high quality. It launched at about $8,000 and sold for $9,000–$11,500 in mid-2026, with some reports far higher since. This is for businesses and researchers, not hobbyists.
Should you rent instead?
If you only need a large model occasionally, renting a cloud GPU by the hour, or using a hosted AI service, can cost far less than a $2,000+ card. Buy local hardware for privacy, for daily use, or to stop paying monthly fees.
Gaming, editing and AI on one card?
An RTX 5070 Ti or 5080 (16GB) is excellent for gaming and video editing. For AI, though, it holds about the same models as the much cheaper RTX 5060 Ti 16GB. If AI matters to you, put the money into memory, not speed. For editing-focused builds, see our video editing computer guide.
FAQ
How much VRAM do I need for AI?
16GB for 8B–14B chat models, 24–32GB for 32B models, and 48GB or more for 70B models (or a unified-memory computer).
Can I use two graphics cards for AI?
Yes. Unlike video editing, tools such as llama.cpp can split one model across two cards. Two 24GB cards together can run a 70B model at Q4.
Is AMD or Intel OK for AI?
For chat models in Ollama, LM Studio or llama.cpp, mostly yes. For image and video generation and most tutorials, NVIDIA is still the easiest path.
Is an 8GB card enough?
Only for small 7B–8B models. We wouldn’t buy a new 8GB card for AI in 2026.
How we chose
We did not benchmark these cards ourselves. Picks are based on manufacturer specifications and prices, AMD’s and NVIDIA’s published figures, independent test results from computingforgeeks (October 2026), and prices reported by technology publications in 2026. We will update this page as prices change. See how we work and our affiliate disclosure.
Sources: computingforgeeks: best GPU for LLMs (Oct 2026) · AMD: Ryzen AI Max+ AI PCs · igor’sLAB: Intel Arc Pro B70 · DGX Spark 64GB/128GB pricing (Oct 2026) · Digital Citizen: RTX PRO 6000 prices · Tech Times: GPU prices, Oct 2026 · Apple: Mac Studio M5
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