The Best GPUs for Local LLMs in 2026
The consumer GPU landscape for local AI inference has shifted dramatically. Here's a practical breakdown of what to buy in 2026 depending on your use case and budget.
Tier 1: Maximum Performance
NVIDIA RTX 5090 (32GB, ~$2000)
The RTX 5090 delivers ~1,800 GB/s memory bandwidth — nearly 3× the 4090. This makes it exceptional for large models and long context windows. At 32GB VRAM, it comfortably runs 70B models at Q4_K_M. The price-to-performance ratio isn't as favorable as the 4090 was at launch, but for pure inference throughput it's unmatched in the consumer segment.
Mac Studio M4 Ultra (192GB Unified Memory)
Apple's unified memory architecture is uniquely suited for LLMs. With 192GB of bandwidth-optimized unified RAM, you can comfortably run 70B models at Q8, or even attempt 180B models at lower quant. The ~400 GB/s memory bandwidth is lower than the 5090, but the effective throughput is competitive for large models that don't fit in discrete VRAM.
Tier 2: Value Pick
NVIDIA RTX 4090 (24GB, ~$1,200 used)
Still the best value for local inference in 2026. 24GB handles 13B–34B models in Q4 with ease, and 70B models with aggressive offloading. The 1,008 GB/s bandwidth keeps inference snappy. Buy used if you can find a clean one.
Tier 3: Budget
RTX 4070 Ti / 4080 (12–16GB)
Solid for 7B–13B models. Can offload larger models to RAM but will experience reduced throughput. Good entry points if you're new to local AI.
What to Avoid
- GPUs with <8GB VRAM — insufficient for anything meaningful beyond 3B models at high quant.
- AMD cards (RX 7900 XTX) — ROCm support in llama.cpp is improving but still lags behind CUDA.
- Older NVIDIA (RTX 3070 and below) — Memory bandwidth is the bottleneck, not VRAM capacity.
Multi-GPU Setups
Two 4090s outperform a single 5090 for most workloads and cost less. NVLink isn't necessary for llama.cpp inference — PCIe 4.0 x16 bandwidth is sufficient. Tensor parallelism via vLLM benefits more from NVLink, but consumer cards don't support it anyway.