GPUFits
Best GPU for Local LLMs in 2026: Every Budget Tier
updated 2026-08-04 · verified 2026-08-04
Prices verified August 2026. The used market moves fast — treat every number here as a dated snapshot.
The one rule that matters
VRAM first, bandwidth second, everything else last. A GPU’s job in inference is to hold the model and read it fast. Compute cores barely matter for single-user generation.
Budget tiers
~$200 — RTX 3060 12GB (used)
The honest entry point. 12GB runs every 7B–8B model at Q4–Q6 with room to spare, and 13B-class models at Q4. 360 GB/s means an 8B generates around 45 tok/s. If you’re just starting with Ollama, this is enough — don’t overbuy before you know your usage.
~$550 — RTX 4070 Ti Super 16GB (used) / RX 7900 XTX 24GB (used, ~$650)
The 16GB tier fits 13B models at Q8 and 24B models at Q4. The AMD alternative gives you 24GB for similar money with 960 GB/s bandwidth — but ROCm support in llama.cpp is workable, not seamless. Buy AMD only if you’re comfortable troubleshooting; the VRAM-per-dollar is real.
~$1,000 — RTX 3090 24GB (used) — the value king
Still the answer in 2026. 24GB at ~$1,000 (eBay sold listings tracked $1,010–1,081 in July 2026) runs everything through Qwen3-32B/Q4. Two of them ($2,000) run Llama-3.3-70B at ~19 tok/s. The catch: it’s a 6-year-old 350W card with zero warranty. Check seller history, stress-test on arrival.
~$1,100–1,800 — RTX 4090 24GB (used)
Same 24GB as the 3090 but 8% more bandwidth and far more compute. Worth it if you also game or do SD/flux image work. For pure LLM inference, the premium over the 3090 buys you ~10% speed, nothing more.
$1,999 — RTX 5090 32GB (new)
The best new card for LLMs, period. 32GB finally makes 32B-class models comfortable on one card, and 1,792 GB/s is a 78% bandwidth jump over the 4090 — token generation scales nearly linearly. The problem is availability: street prices ran $2,500–3,200 through mid-2026. Worth it at MSRP, hard to justify at a 40% markup.
$2,899–5,299 — Mac Studio (M4 Max 64GB / M3 Ultra 96GB)
A different philosophy: enormous unified memory, silent, ~100W. The 96GB M3 Ultra holds gpt-oss-120b or Llama-70B at Q8 — impossible on any single NVIDIA consumer card. But bandwidth (819 GB/s) means a 70B model generates at roughly 10–15 tok/s, and the Apple tax is real. Note: Apple discontinued the 256GB/512GB M3 Ultra configs in 2026 (DRAM shortage) — 96GB is now the ceiling, and the base price rose to $5,299.
What to skip
- 8GB cards (4060, 5060, 3070): fine for games, dead on arrival for modern LLMs.
- H100/A100 for personal use: $15k–30k buys datacenter features you don’t need. Rent them by the hour instead.
- Mining-grade used cards under $500: the savings don’t survive one dead card.
Quick answers
| I want to run… | Buy |
|---|---|
| 7B–8B models | used RTX 3060 12GB |
| 13B–32B models | used RTX 3090, or RTX 5090 at MSRP |
| 70B models | 2× used 3090, or Mac Studio 96GB |
| 120B MoE models | Mac Studio 96GB, or 4× 3090 |
| DeepSeek-R1 671B | nothing — rent cloud GPU |
FAQ
What's the single best value GPU for local LLMs in 2026?
A used RTX 3090. 24GB of VRAM for around $1,000 — enough for every model up to 32B at Q4, and two of them handle 70B. Nothing else comes close on dollars per GB.
Is the RTX 5090 worth it over a used 4090?
If you can get one near its $1,999 MSRP: 32GB and 78% more bandwidth than the 4090 means Qwen3-32B becomes comfortable instead of tight, and everything generates ~75% faster. But at the $2,500+ street prices common in 2026, a used 4090 plus change is the saner buy.
Should I buy a Mac or a GPU rig for LLMs?
Mac if you prioritize silence, power efficiency, and big unified memory for huge MoE models. NVIDIA if you prioritize speed per dollar and the CUDA ecosystem. See our Mac vs GPU guide for the full breakdown.