CUDA Kernels

Event Core A significant performance breakthrough has been achieved in the Local LLM community: running the Qwen3.6-35B-A3B model on a budget-friendly RTX 3060 12GB GPU. By leveraging spiritbuun's specialized llama-cpp branch and mudler's APEX quantization, the setup achieved a generation speed of 37 t/s even with a 72k context fill, pushing the boundaries of what consumer-grade silicon can handle. ▶ MoE Efficiency at Scale: The Qwen3.6-35B MoE (Mixture of Experts) architecture, with only 3B active parameters, proves to be the "silver bullet" for high-reasoning tasks on memory-constrained hardware. ▶ Kernel-Level Optimization: The integration of Fused MMA fixes, TurboQuant, and Flash Attention (fattn) improvements allows for aggressive offloading of a 17.3GB model onto 12GB of VRAM without the typical performance cliff. Bagua Insight This is a watershed moment for the democratization of long-context GenAI. The ability to process 128K context windows on a sub-$300 GPU signals that the "VRAM Wall" is being dismantled not by hardware manufacturers, but by the open-source software ecosystem. We are seeing a shift where software-defined inference optimizations (like APEX and TurboQuant) are effectively extending the lifecycle of mid-range hardware by 2-3 years. For the industry, this validates that MoE is the superior architecture for local deployment, offering the reasoning depth of a 35B model with the compute footprint of a 3B model. Actionable Advice Enterprises looking to minimize TCO (Total Cost of Ownership) for local RAG pipelines should pivot away from dense models and prioritize MoE architectures optimized via APEX quantization. Developers should integrate these specialized CUDA kernels into their production stacks immediately to extract maximum throughput from existing hardware. If you are still waiting for H100 allocations for basic RAG tasks, you are overspending—optimized consumer hardware is now a viable alternative for high-context inference.