GPU Infrastructure

Tech Radar: vLLM Context-Aware Routing & MLA KV Cache Architecture Answer-First: Multi-Head Latent Attention (MLA) combined with Context-Aware Prefix Routing in vLLM resolves the VRAM memory wall in multi-turn agent execution loops. Compressing the Key-Value cache into low-dimensional latent vectors and routing shared-prefix tool calls to the matching GPU worker reduces VRAM consumption by 75.8% and slashes Time-to-First-Token (TTFT) from 840ms to 165ms. 1. The VRAM Explosion in Autonomous Agent Multi-Turn Loops When scaling autonomous AI agent systems (automated code reviewers, SQL analytics swarms, customer support bots), inference pipelines execute iterative loops: $$\text{User Query} \longrightarrow \text{Tool Invocation} \longrightarrow \text{Observation} \longrightarrow \text{Next Tool} \dots \longrightarrow \text{Final Answer}$$ ...

High-throughput local LLM architecture guide combining vLLM PagedAttention virtual memory, Prefill-Decode disaggregation over RoCE v2/NVLink, and a custom Go API Gateway with SHA256 prompt prefix context-affinity routing, zero-allocation SSE streaming, and 71% cost savings over SaaS APIs.

High-Throughput Local LLM Infrastructure: Architecting a Distributed Go API Gateway for vLLM & PagedAttention Clusters Answer-first: High-throughput local LLM infrastructure pairs vLLM continuous batching inference servers with a Go API gateway for dynamic request queuing, load balancing, and token rate limiting. Implementing this architecture enforces sub-50ms P99 latency guarantees, zero-allocation memory pooling with Go 1.24 unique.Handle, and fault-tolerant Dapr 1.15 component orchestration for resilient production scaling. Executive Summary & Architecture Overview Operating open-weight Large Language Models (e.g., Llama-3-70B, DeepSeek-R1, Mistral-Large) at enterprise scale (>20M to 500M+ tokens/day) introduces severe architectural and economic bottlenecks when relying solely on public SaaS APIs. While proprietary APIs provide simple HTTP interfaces, they present two main issues: runaway API expenditures that scale linearly with volume and strict data privacy/compliance boundaries that prohibit transmitting sensitive enterprise IP across public boundaries. ...