disclosures, OpenAI (12/24). ChatGPT figures are estimates per company disclosures of ~1B daily queries Annual Searches by Year (B) Since Public Launches of Google & ChatGPT – 1998-2025, per Google running models at scale in real-time. Inference happens constantly, across billions of prompts, queries, and decisions, whereas model training is episodic. As Amazon CEO Andy Jassy noted in his April – and as usage increases, so does demand for compute. We’re seeing it across every layer: more queries, more models, more tokens per task. The appetite for AI isn't slowing down. It’s growing into every

Multi-Head Attention (MHA) Multi-Query Attention (MQA) Multi-Head Latent Attention (MLA) Keys Queries Values projection Compressed Latent KV Cached During Inference Figure 3 | Simplified illustration low-rank compression for the queries, even if it cannot reduce the KV cache: c? ? = ? ??h?, (12) q? ? = ???c? ? , (13) where c? ? ∈ R?′ ? is the compressed latent vector for queries; ?′ ?(≪ ?ℎ?ℎ) denotes the dimension; and ? ?? ∈ R?′ ?×?,??? ∈ R?ℎ?ℎ×?′ ? are the down-projection and up- projection matrices for queries, respectively. 2.1.3. Decoupled Rotary Position Embedding Following DeepSeek 67B (DeepSeek-AI,

run( triage_agent, ( ) ) "You act as the first point of contact, assessing customer queries and directing them promptly to the correct specialized agent." "Could you please provide an update Relevance classifier Ensures agent responses stay within the intended scope 
 by flagging off-topic queries. For example, “How tall is the Empire State Building?” is an 
 off-topic user input and would be