Best GPU for Local LLMs in 2026

Two and a half years after launch, the RTX 4090 remains the single best value in 24 GB inference cards. Used-market prices have settled in the $1,200–$1,500 range, with the original $1,599 MSRP still listed at major retailers but rarely paid. For roughly 70% of the price of an RTX 5090 you get 75% of the 8B throughput and identical 24 GB VRAM, which means identical model compatibility for nearly every common workload.

The 4090's weakness is bandwidth, 1.0 TB/s vs the 5090's 1.79 TB/s , which shows up most painfully on large-context workloads. Once you push past 16k context the KV-cache traffic dominates and the 5090 pulls ahead by a wider margin. For everyday chat, coding agents, and 8B–34B workloads, the 4090 remains the reasonable buy.

The original 24 GB consumer card from late 2020 remains the entry point for serious local-LLM work. Used 3090s have settled to $700–$900, and a pair with NVLink can host Llama 3 70B at Q4_K_M for under $2,000 of GPU spend. It is slower per-card than a 4090 by roughly 1.5×, but in the dollars-per-VRAM-gigabyte race it remains unbeatable.

The NVLink 3.0 bridge, required if you want the pair to share VRAM efficiently for tensor-parallel inference , is itself an artifact of a vanished era; Ada and Blackwell consumer cards dropped NVLink entirely. Bridges are still available from Nvidia partners and used markets. Expect to pay $150–$300 for a working 4-slot SLI HB bridge.

48 GB of ECC VRAM in a two-slot, 300 W blower-style card. The RTX 6000 Ada is the only sane choice when you need to host Llama 3 70B at Q8 in one slot, or stack two cards into a multi-GPU workstation without space and power blowing up. It is slower per-card than a 5090 on pure tok/s, but the extra VRAM unlocks workloads the 5090 cannot touch.

The price, $6,000–$7,500 , pushes this card into "Is this for my job?" territory. If the answer is yes, it pays for itself in saved cloud-GPU hours within months. If the answer is no, look at a pair of used RTX 3090s or a Mac Studio with 128 GB unified memory before signing this PO.

AMD's flagship RDNA3 card brings 24 GB VRAM at consumer prices and, as of ROCm 6.2 and the llama.cpp Vulkan backend reaching feature parity, is finally a real option for non-CUDA users. On Llama 3 8B Q4_K_M we measure 76 tok/s on ROCm and 71 tok/s on Vulkan, roughly 60% of an RTX 4090's speed at 70% of the price.

The CUDA-only stack remains real. vLLM, TensorRT-LLM, exllama-v2 with the fastest FlashInfer kernels, and the polished bitsandbytes integration are all NVIDIA-locked. If you live exclusively in llama.cpp and Ollama, AMD is a perfectly fine choice. If you wander into the broader research ecosystem, CUDA still wins.