Best NVIDIA GPUs in 2026
Best GPUs under ¥45,000 for everyday use
NVIDIA GeForce RTX 2060 SUPER 8GB GDDR6
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These options also score highly and may better suit your specific needs
No GPUs under ¥75,000 yet. See our top picks under ¥120,000
Best GPUs under ¥120,000 for everyday use
NVIDIA GeForce RTX 3060 Ti 30 Series 8GB GDDR6
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NVIDIA Quadro RTX 5000 16GB GDDR6 workstation
NVIDIA 900-5G172-2580-000
Best GPUs under ¥180,000 for everyday use
NVIDIA GeForce RTX 3080 10GB GDDR6X
Premium GPUs for everyday use
NVIDIA 900-1G144-2545-000
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NVIDIA GeForce RTX 4080 Founders Edition 16GB GDDR6X
Why we recommend this ▼
The Ada Lovelace architecture and 16GB of GDDR6X memory on a 256-bit bus deliver a 24.72 FP32 TFLOPS compute ceiling, making 4K gaming and ray tracing fluid at high frame rates. DLSS 3 frame generation and AV1 encoding provide a meaningful uplift in supported titles and streaming workflows, while the Founders Edition cooler keeps the 320W card within a manageable 3-slot footprint. This GPU is best for 1440p and 4K gamers who want top-tier ray tracing performance without stepping up to the flagship RTX 4090.
NVIDIA Quadro RTX 5000 32GB GDDR6 workstation
Why we recommend this ▼
Built on the Ada Lovelace architecture, its 32GB of GDDR6 memory and 12,800 CUDA cores deliver petaFLOPS-level inferencing for complex AI and rendering workloads. Unique professional features like NVIDIA GPUDirect RDMA support and Quadro Sync II compatibility set it apart for multi-GPU and large-scale display environments. This card is best for data scientists training large language models and engineers running multi-application simulation pipelines.
NVIDIA Blackwell GPU Jetson Thor 128GB workstation
Why we recommend this ▼
Equipped with a 2560-core NVIDIA Blackwell GPU and 96 fifth-gen Tensor Cores, this developer kit delivers 2070 TFLOPS of AI performance for demanding model training. The 128GB memory pool and dedicated AI acceleration make it a specialized tool for edge computing and robotics prototyping. This kit is best for AI researchers and robotics engineers developing and deploying large language models directly on-device.
