Best GPU for Running Local LLMs in 2026

GPU selection for local LLMs comes down to three variables: VRAM (determines which models fit), memory bandwidth (determines inference speed), and price. More VRAM is almost always better — but the relationship between bandwidth and tok/s is what most buyers miss.

1. 1.RTX 4090 24GB — Best single GPU. 82.6 GB/s bandwidth. ~85-95 tok/s on Llama 3.1 8B Q4.
2. 2.RTX 4080 Super 16GB — 90% of 4090 performance, $400 cheaper. Strong value.
3. 3.RTX 4070 Ti Super 16GB — Best value pick. 16GB VRAM, excellent bandwidth.
4. 4.RX 7900 XTX 24GB — AMD's best. 24GB for less than RTX 4090. ROCm support improving.
5. 5.RTX 3090 24GB — Used market bargain. Same VRAM as 4090, older but capable.
6. 6.Apple M3 Max (64-96GB unified) — Best for 70B models. Unified memory is game-changing.
7. 7.RTX 4060 Ti 16GB — Best budget 16GB option. Slower bandwidth but great VRAM.
8. 8.RTX 4060 8GB — Entry-level. Handles all 7B models. Great starting point.

LLM inference is memory-bandwidth-bound, not compute-bound. During the generation phase, the GPU loads model weights from VRAM on every single token. A GPU with higher bandwidth generates tokens faster even at the same VRAM capacity.

• ▸RTX 4090: 1,008 GB/s bandwidth → fastest consumer GPU for inference
• ▸RTX 4080: 736 GB/s bandwidth → 73% of 4090 speed on large models
• ▸RTX 4070 Ti Super: 672 GB/s bandwidth → excellent for 16GB
• ▸RTX 4060 Ti: 288 GB/s bandwidth → half the speed of 4080 despite similar VRAM
• ▸Apple M3 Max: 400 GB/s bandwidth → competitive with RTX 4080 for unified models

Apple M-series chips use unified memory shared between CPU and GPU. An M3 Max with 64GB can load a 70B model in Q4 entirely into memory — something that requires dual RTX 4090s on a PC. The trade-off is lower raw bandwidth than a dedicated 4090, but for 70B models it's the most accessible option.

• ▸Under $300 — RTX 4060 8GB. Runs all 7B models fast. Perfect starter.
• ▸$400-600 — RTX 4070 8GB or used RTX 3080 10GB. More headroom.
• ▸$600-900 — RTX 4070 Ti Super 16GB. Best value overall.
• ▸$1,000-1,500 — RTX 4080 Super 16GB or used RTX 3090 24GB.
• ▸$1,800-2,000 — RTX 4090 24GB. The top pick if budget allows.
• ▸No ceiling — Apple M3 Ultra (192GB). Runs anything. Laptop-friendly.

Before committing to a GPU, check runyard.dev with the specs of the card you're considering. The Model Radar will show exactly which models fit, at what quantization, and what real-world tok/s to expect — so you know your purchase is right for your workload.