cuda-13

MSVC+CUDA llama.cpp fork for Windows: KDA/GDN, long-context KV placement, TurboQuant/TCQ when measured, speculative draft. Lab defaults — TriAttention opt-in only, not recommended.

Production-grade SGLang inference on NVIDIA DGX Spark / GB10 (sm_121) — sm_121a-native sgl-kernel, model-agnostic launch recipes, wedge handling.

Pre-built PyTorch wheels and build scripts for NVIDIA DGX Spark (GB10, sm_121, Blackwell, CUDA 13.0, ARM64)

Measured sm_110 / sm_110a facts for NVIDIA Jetson AGX Thor: tensor-core capability matrix (2:4 sparsity works on plain sm_110; tcgen05 + NVFP4 block-scale is sm_110a-only), CUDA 11/12->13 migration breaks, CPU ISA, and the bandwidth roofline. With probes.

Build the GPU inference stack from source, repeatably: for Blackwell sm_120 on CUDA 13.x and Python 3.14. Machine-readable build state, generated patches, and an abductive-triage skill for build failures.

Windows NVIDIA-only Triton 3.7.0 build pipeline for RTX 5090 / Blackwell sm_120a, with FP8 tl.dot validation and peak benchmark results.

Hunyuan3D-2 fork — image→textured 3D→sliced STL + part segmentation. RTX 50-series (Blackwell/sm_120), CUDA 13.0, Python 3.12, PyTorch 2.11+cu130.

NATTEN 0.21.6 Windows wheel for Python 3.13, PyTorch 2.10, CUDA 13.0, NVIDIA Blackwell sm_120 / RTX 5090; Pixal3D-ComfyUI compatible.

Automated Docker build and serving stack for vLLM with native NVFP4 KV cache quantization on NVIDIA Blackwell (RTX 5090 / SM120)

Accelerate Kimi Delta Attention computations with high-performance CUTLASS kernels designed for NVIDIA SM90 architectures and beyond.