Guides · Hardware · 7 min read
The best GPU for running LLMs locally in 2026
If you want to run AI models on your own computer, the graphics card matters more than anything else in the machine. Not because of gaming benchmarks or ray tracing, but because of two numbers that most spec sheets bury: how much memory the card has, and how fast it can read that memory. This guide ranks the cards worth buying for local LLMs right now, at every budget.
The two numbers that matter
VRAM decides what you can run. A model has to fit in the card's memory to run at full speed. An 8B model at Q4 needs about 5.5 GB, a 14B model about 9 to 11 GB, a 32B model around 20 GB, and a 70B model over 40 GB. If it does not fit, it spills into system RAM and slows to a crawl. Our VRAM guide explains the math.
Memory bandwidth decides how fast it runs. Each new word the model writes requires reading most of the model from memory. A card with twice the bandwidth produces roughly twice the tokens per second on the same model. This is why an older RTX 3090 still beats many newer cards for AI.
Everything else, including CUDA cores and clock speed, matters much less for chatting with a model.
Quick picks
| Card | VRAM | Bandwidth | Approx. price | Runs well |
|---|---|---|---|---|
| Intel Arc B580 | 12 GB | 456 GB/s | $250 to $290 new | 8B to 14B at Q4 |
| RTX 3060 12GB | 12 GB | 360 GB/s | $200 to $250 used | 8B to 14B at Q4 |
| RTX 5060 Ti 16GB | 16 GB | 448 GB/s | $430 to $480 new | 14B at Q5, 20B to 24B at Q4 |
| RTX 3090 (used) | 24 GB | 936 GB/s | $700 to $1,000 used | 32B at Q4, 70B at Q2 (tight) |
| RTX 5080 | 16 GB | 960 GB/s | $1,000 to $1,100 new | Fast 14B to 24B |
| RTX 4090 | 24 GB | 1,008 GB/s | $1,400 to $1,700 used | 32B fast, 70B at Q2 |
| RTX 5090 | 32 GB | 1,792 GB/s | $2,000 to $2,200 new | 32B at Q8, 70B at Q3 |
Prices are typical US street prices in 2026 and move a lot. Check current listings before you buy.
Under $300: Intel Arc B580 or a used RTX 3060 12GB
Both give you 12 GB, which is the real entry point for local AI. That is enough for the strongest 14B models at Q4, such as Qwen3 14B and Phi-4, at around 25 to 35 tokens per second. The Arc B580 is new, has more bandwidth and costs about the same as a used 3060. The catch is software: Intel support in Ollama and LM Studio has improved but is still behind Nvidia, and some tools work best on Linux. If you want everything to just work, a used RTX 3060 12GB is the safer pick. Avoid the 8 GB versions of any card if AI is the main reason you are buying.
Around $450: RTX 5060 Ti 16GB
The best new card for most people. 16 GB lets you run 14B models at higher quality, gpt-oss 20B, and 24B coding models like Devstral Small at Q4. It draws about 180 W, so it fits in almost any desktop without a new power supply, and it comes with a warranty. Its weakness is bandwidth: at 448 GB/s it is roughly the same speed as the much older 3090 on small models and falls behind on anything big. Make sure you buy the 16 GB version, not the 8 GB one with the same name.
The value king: a used RTX 3090
24 GB of VRAM and 936 GB/s for the price of a mid-range new card. That is enough to run dense 32B models like Qwen3 32B and DeepSeek-R1 32B at Q4, which is a real step up in quality from 14B. It is also fast: around 45 to 50 tokens per second on 8B models. The downsides are age and power. Most used 3090s are four or five years old, many were used for crypto mining, and there is no warranty. It needs a 750 W power supply and gets hot. Buy from a seller who lets you test it, and check that the fans and memory temperatures are healthy.
High end: RTX 4090 and RTX 5090
The 4090 has the same 24 GB as a 3090 but is noticeably faster, and used prices have come down. The 5090 is the only consumer card with 32 GB and nearly 1,800 GB/s, which makes it about twice as fast as a 3090 and lets you run 32B models at Q8 or 70B models at a low quantization on a single card. If you run models all day for coding or agents, the speed is worth it. For occasional chat, it is not.
What about AMD?
The RX 7900 XTX (24 GB) and RX 9070 XT (16 GB) have good memory specs for the money, and Ollama and LM Studio support them. Expect a little more setup, especially on Windows, and slightly lower speeds than an Nvidia card with similar bandwidth. If you are comfortable with that, the 7900 XTX is a solid 24 GB alternative to a used 3090 with a warranty.
Cards to skip
Any 8 GB card bought specifically for AI, including the RTX 5060 and 4060. The RTX 5070 is fast but has only 12 GB, which puts it in the same model tier as a much cheaper 3060. And laptop GPUs with the same name as desktop cards often have less memory and much lower bandwidth, so check the exact VRAM.
Should you just buy a Mac instead?
If you want to run models bigger than 32B, a Mac with 64 GB or more of unified memory is often cheaper than the equivalent Nvidia setup, though slower per token. Our Mac vs Nvidia comparison covers the trade-off. Before you spend anything, put the card you are considering into the checker to see exactly which models it will run and how fast.