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Artemarius / spbitnet

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Custom CUDA kernels for accelerating 1.58-bit ternary LLM inference with 2:4 structured sparsity on consumer Ampere GPUs. Exploits both ternary arithmetic (no multiplies) and hardware sparse tensor cores to maximize throughput on RTX 3060. Based on the Sparse-BitNet paper (Zhang et al., 2026).

sparsity gpu cuda transformer cpp17 cuda-kernels quantization ampere structured-sparsity bitnet ternary-weights cusparselt llm-inference
  • Updated Mar 11, 2026
  • Cuda

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