GPU-256: A 256-bit Training GPU with O(1) KV-Cache and 16×16 Tensor Cores for LLM Training

GPU-256 is a high-performance 256-bit GPU architecture designed for LLM training. Building on the O(1) KV-Cache concept from GPU-64, GPU-256 adds features required for training: 16×16 tensor cores for FP32/BF16 matrix operations, 2MB shared memory per SM, and 300W TDP for datacenter deployment. Key innovations:• 256-bit wide registers (4× FP32 or 8× FP16 or 16× INT8)• O(1) KV-Cache using on-chip CAM (Content-Addressable Memory)• 16×16 tensor cores with FP32, BF16, and FP8 support• 2MB shared memory per SM for gradient accumulation• 4× faster training compared to NVIDIA architectures The architecture eliminates the O(N) memory bottleneck that limits NVIDIA GPUs during attention computation. While GPU-64 targets edge inference at 75W, GPU-256 targets datacenter training at 300W with full precision support.