How are u guys designing your workflow for research/ learning right now? Any particular approach that stands out? Currently I am trying out 1. Prompt generation from research idea with a perspective of a expert in the space 2. Passing that prompt to deep research 3. Passing that generation in pdf to notebooklm E.g research about gpu from a high level perspective like Vinod kholsa and deep engineering perspective from a hardware engineer

### **Prompt for Comprehensive Research: The LLM Inference Stack** **Objective:** Generate a detailed, multi-faceted analysis of the full-stack technology and business landscape for Large Language Model (LLM) inference. The analysis must be framed for a technically-astute venture investor and operator, adopting a first-principles, systems-thinking approach in the style of Vinod Khosla. The final output should be a strategic memo that dissects the ecosystem from three integrated perspectives: 1. **The Engineer's Perspective:** The fundamental technology and its bottlenecks. 2. **The Venture Investor's Perspective:** The market structure, points of disruption, and asymmetric opportunities. 3. **The Business Strategist's Perspective:** The value chain, business models, and long-term strategic plays. --- ### **Detailed Research Queries by Perspective:** **Part 1: The Engineer's Perspective — "What is the System and Why is it Hard?"** * **Hardware Foundation:** * Detail the critical hardware components for production-grade LLM inference (GPUs, CPUs, Memory, Interconnects). * Compare the key data center GPUs (e.g., NVIDIA H100/A100, AMD MI300X) on relevant metrics for inference: memory bandwidth, capacity, and specialized compute units (Tensor Cores). * Explain the fundamental technical bottleneck: Why is LLM inference primarily a **memory-bound** problem, not a compute-bound one? * **Software & Optimization Layer:** * Analyze the role of inference servers and engines. What are the core innovations of leading open-source solutions like **vLLM** (e.g., PagedAttention, continuous batching) and proprietary solutions like **NVIDIA's TensorRT-LLM**? * Describe the essential model optimization techniques used to improve performance, including **quantization**, **speculative decoding**, and the different forms of **parallelism** (tensor, pipeline). **Part 2: The Venture Investor's Perspective — "Where is the Disruption and Value Accretion?"** * **Market Mapping & Incumbency:** * Identify the primary incumbents and their moats. How defensible is **NVIDIA's** position with its CUDA ecosystem? What is the strategic play for hyperscalers like **AWS Bedrock, Azure OpenAI, and Google Vertex AI**? * Map the key "insurgents" or specialized inference providers (e.g., **Groq, Together AI, Fireworks AI, Perplexity, Anyscale**). What is their unique angle of attack—custom silicon, software optimization, or novel business models? * **Investment Theses & "Science Experiments":** * What are the most compelling "asymmetric bet" opportunities? Focus on: 1. **Novel Hardware:** Companies developing new chip architectures (LPUs, etc.) designed specifically for inference. 2. **Software Abstraction:** Ventures creating software that unlocks performance on cheaper, non-NVIDIA, or commodity hardware. 3. **Algorithmic Breakthroughs:** Fundamental research in areas that could radically reduce the computational or memory cost of inference. * Analyze the "picks and shovels" plays. Which companies are building the critical **LLMOps and orchestration layers** (e.g., Portkey) that manage cost, routing, and reliability across multiple model providers? **Part 3: The Business Strategist's Perspective — "How Do You Win and What is the Endgame?"** * **Value Chain Analysis:** * Deconstruct the LLM inference value chain, from silicon manufacturing to the end-user application. Where is the majority of the value being captured today, and where is it likely to shift in the next 5-10 years? * Analyze the competing business models: managed API services, dedicated deployments, and peer-to-peer compute networks. What are the pros and cons of each? * **Strategic Outlook & The "Chindia Test":** * What is the path to radically lower costs for inference? Which players are best positioned to make high-performance inference cheap enough to become a global,