Local AI

The Best GPU for Local LLMs in 2026 (Tested Picks Under $300, $800, $1500)

After testing 7 GPUs, here are the best picks by budget for running Llama 3, Qwen 2.5 and DeepSeek locally. RTX 3060 used at $200 is still the entry-level winner.

“What GPU should I buy for running LLMs?” is the single most-asked question in local AI. The honest answer depends on exactly one thing: your budget. Below, the best GPU at each price tier in 2026, with real-world performance numbers, what you can run, and when to spend more vs. less.

Quick answer: Under $300 → used RTX 3060 12GB (Llama 3 8B at 45 tok/s). $500–800 → RTX 4070 Super for speed or 4060 Ti 16GB for capacity. $1000–1500 → used RTX 3090 24GB (unlocks 70B Q2). $2000+ → 2× RTX 3090s for 48 GB total (70B at Q5 comfortably).

Decision flowchart to pick the right GPU for running local LLMs by budget tier — Match your budget to the right GPU — each tier unlocks a different class of models.

Budget under $300 — Used RTX 3060 12GB

★ Best value entry-level pick

The 3060 12GB is unmatched under $300. You get 12GB of VRAM (more than any current-gen sub-$500 card), run Llama 3.1 8B at 45 tok/s, Qwen 2.5 14B at ~22 tok/s, and even CodeLlama 13B for serious coding. Used prices on eBay / Facebook Marketplace hover around $180-220 in 2026.

What you can run comfortably

Llama 3.1 8B — 45 tok/s
Qwen 2.5 14B — 22 tok/s
Mistral 7B — 50+ tok/s
CodeLlama 13B — 20 tok/s
Phi-3 Medium 14B — 20 tok/s

Budget $500-800 — RTX 4070 Super 12GB OR 4060 Ti 16GB

⚡ Strongest new-GPU value

If you want a warranty, pick between speed and VRAM. 4070 Super (12GB) is ~40% faster than a 3060 for the same 8B-13B models. 4060 Ti 16GB trades speed for capacity — lets you run 32B Q3 quant that the 4070 can’t fit. For coding, pick 4070 Super; for quality with larger models, pick 4060 Ti 16GB.

4070 Super: speed king of the tier

Llama 3.1 8B — 75 tok/s
Qwen 2.5 14B — 40 tok/s
Can’t fit: 32B+ models

4060 Ti 16GB: capacity king

Qwen 2.5 32B Q3 — 8 tok/s (slow but runs!)
Gemma 2 27B — 15 tok/s
Mixtral 8x7B Q4 — 18 tok/s

Budget $1000-1500 — Used RTX 3090 24GB

👑 Power-user pick — best VRAM per dollar

The 3090 unlocks 70B models. At Q2 you get 10 tok/s on Llama 3.1 70B — slow but usable for deep reasoning tasks. At Q5 you run 32B models at 28 tok/s, which is close to GPT-4-class quality for general chat. Used prices $700-900 in 2026. No new NVIDIA card offers 24GB under $1500, so used 3090 is the undisputed value pick.

Llama 3.1 70B Q2 — 10 tok/s (the “it runs!” achievement unlocked)
Qwen 2.5 32B Q5 — 28 tok/s (near-GPT-4 quality, usable speed)
Mistral Large Q2 — 8 tok/s
All 7B-13B models — 55-95 tok/s (overkill-fast)

Budget $2000+ — 2× RTX 3090 OR RTX 6000 Ada 48GB

👑👑 Prosumer / serious researcher

Dual 3090s at 48GB total unlocks Llama 3.1 70B at Q5 quant running 30+ tok/s — genuinely near-GPT-4 quality at full speed. Requires a beefy PSU (1200W+), a motherboard with two x16 slots, and NVLink if you want to pool VRAM. Alternative: RTX 6000 Ada at 48GB is a single card but costs $6000+.

Alternative paths

🍎 Apple Silicon M3 Max / Ultra

Unified memory lets you stretch — M3 Max 64GB runs Llama 3 70B that no 24GB PC GPU can. But speed tops out around 5 tok/s for 70B vs. a 4090’s 18. Best if you already own a Mac or hate fan noise.

🔴 AMD RX 7900 XTX 24GB

Cheaper than used 3090, ROCm support is finally solid on Linux. ~20% slower than 3090 at the same tasks. Avoid on Windows — ROCm stability is still rough.

☁️ Cloud GPU rental

RunPod / Vast.ai rent A100 40GB at $0.50-1.50/hour. Makes sense if you only need LLMs a few hours a week. Break-even vs. owning a 3090 is around 400-600 hours of use per year.

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Last updated: 2026-04-22.