Move far beyond superficial prompting. Master custom OpenAI Triton GPU kernels, GraphRAG with community detection, 4-bit QLoRA, and Model Context Protocol (MCP) agents.
@triton.jit def flash_attn_v3(Q, K, V, sm_scale):Deconstruct transformers from the bottom up. Master Byte-Pair Encoding, KV caching, attention compute patterns, and deploy lightning-fast local inference clusters.
Move beyond naive cosine similarity. Architect hybrid search with Reciprocal Rank Fusion, implement Microsoft GraphRAG with community detection, and build multi-vector visual retrieval with ColPali.
Take full ownership of model weights. Master low-rank matrix decomposition, 4-bit normal float quantization, ultra-efficient Unsloth training loops, and preference alignment via DPO without RL reward models.
Move past toy agent frameworks. Design production multi-agent swarms using LangGraph, deterministic state persistence, sandboxed execution, and Anthropic's Model Context Protocol (MCP).
Break through the memory bandwidth wall. Understand NVIDIA GPU architecture (SMs, warps, tensor cores), write fused Triton kernels, and tune multi-GPU clusters running vLLM and TensorRT-LLM.
Architect high-throughput multi-provider gateways with dynamic latency hedging, Redis token-bucket rate limiting, tiered semantic caching, and GPU capacity planning.
Most tutorials demonstrate simple API calls. We teach you how to write the kernels, eliminate memory bottlenecks, and scale distributed inference clusters.
Understand how GPU Streaming Multiprocessors, Warps, SRAM caches, and Tensor Cores execute matrix operations and attention heads.
Zero pseudocode. Every lesson includes executable Python, PyTorch, and OpenAI Triton kernels with 1-click cloud GPU environments.
Master prompt boundary attacks, sandboxed MCP containers, human-in-the-loop approvals, and continuous evaluation suites.
Straight answers on our non-certificate engineering philosophy, hardware costs, and hands-on environments.
No. We deliberately do not provide paper certificates or vanity diplomas. In high-throughput AI systems engineering, generic certificate PDFs carry zero market credibility. Instead, LearnLLM focuses 100% on reproducible PyTorch and Triton code, custom GPU kernels, and measured latency/throughput benchmarks that demonstrate verifiable competence in production environments.
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