Best GPU for local LLMs (2026)
Updated June 2026
The honest version: VRAM decides what you can run, price decides what you should buy. Here are the picks by budget and model size — then check live $/GB-VRAM to time it through the shortage.
The one rule: VRAM first
A local LLM has to fit in video memory to run well. If the weights fit in VRAM, generation is fast; if they don't, the model spills into system RAM and slows down by 5-20×. So the first question is always "how much VRAM?", and the value metric that matters is dollars per GB of VRAM — which is exactly how our GPU tracker ranks cards.
How much VRAM for which model (Q4)
| VRAM | Runs (4-bit) |
|---|---|
| 12 GB | ~12-14B models |
| 16 GB | ~14B comfortably; 27B+ needs offload |
| 24 GB | ~32B models |
| 32 GB | 32B with room / faster |
| 48 GB (2×24) | ~70B models |
| 96-128 GB | 70B with long context; up to ~200B (Spark) |
Approximate, at Q4 quantization; FP16 needs ~4× the VRAM. Context length adds more.
The picks
Best overall (new): RTX 5090 — 32GB
The fastest single consumer card and the most VRAM in the GeForce line. Ideal if you want top single-GPU speed for up to ~32B (and 70B across two). The catch is price — flagships are scalped well above MSRP in 2026.
Best new value: RTX 5080 / 5070 Ti — 16GB
16GB of fast GDDR7 runs up to ~14B comfortably (27B+ needs CPU offload or a smaller quant) at a fraction of flagship cost. The 5070 Ti is the same VRAM and feature set as the 5080 for less — the sleeper value pick for single-GPU local AI.
Best $/GB-VRAM (new): RTX 5060 Ti 16GB
Currently the lowest dollars-per-GB-VRAM of any new GeForce card — the budget entry into 16GB AI without the flagship tax. (AMD/Intel 16GB cards can be cheaper per GB if you don't need CUDA.)
Best value overall: used RTX 3090 — 24GB
The community value king. Same 24GB as a 4090 at roughly a third of the used price; slower, but for "does the model fit and run" it's unbeatable on $/GB. (Used market — we don't price-track it, but watch it.)
Maximum VRAM: RTX PRO 6000 (96GB) & DGX Spark (128GB)
For 70B+ on one device: the workstation RTX PRO 6000 Blackwell fits a 70B model entirely in 96GB of real GDDR7 VRAM, and the DGX Spark appliance holds 128GB of unified memory for models up to ~200B (high capacity, lower bandwidth than VRAM). Both are pricey — and the PRO 6000's own price has surged in the 2026 memory crunch — so check the live $/GB-VRAM table to see how it stacks up against the (also-scalped) flagships.
Live GPU prices ($/GB VRAM)
How to choose, quickly
- Just starting / tight budget: used RTX 3090 (24GB) or new RTX 5060 Ti 16GB.
- One do-it-all card: RTX 5080 / 5070 Ti (16GB) for ≤14B, or a 24GB card for ~32B.
- Max single-GPU speed: RTX 5090 (32GB), if the price is bearable.
- 70B+ at home: dual 24GB cards, RTX PRO 6000 (96GB), or DGX Spark (128GB).
Best GPU for AI FAQ
What is the single most important GPU spec for local LLMs?
VRAM. If a model fits in VRAM it runs fast; if it does not, it spills to system RAM and slows 5-20×. Memory bandwidth is the tie-breaker once it fits. So buy the most VRAM you can afford at a sane price, then optimize for speed.
What GPU runs a 70B model locally?
About 48GB of VRAM at Q4 — practically two 24GB cards (dual RTX 3090 or 4090), or a single 96GB RTX PRO 6000 / a 128GB DGX Spark appliance. A single 24-32GB card handles up to ~32B at Q4; 70B needs the extra capacity or heavy offload to system RAM.
Is the RTX 5090 worth it for AI?
It is the fastest single consumer card (32GB GDDR7) and the best choice if you want maximum single-GPU speed and can stomach the price — which is heavily inflated in 2026. If you mainly need the model to fit, a 24GB card (used 3090 or 4090) gets you most of the way for far less.
Cheapest way to get into local AI?
A used RTX 3090 (24GB) is the long-standing value pick — the same VRAM as a 4090 at roughly a third of the used price. New, the RTX 5060 Ti 16GB has the best dollars-per-GB-VRAM of current GeForce cards.