Alexa's Input (AI) · 2026-06-01 · 24,219 views · 🔥 1,345/day
AI inference stopped being a model-serving problem and became a distributed systems fight over memory, routing, and latency. Rob Shaw lays out how vLLM, PagedAttention, and llm-d made KV cache efficiency and orchestration central to scaling LLM workloads. That matters because long-context agents and enterprise deployments now live or die on inference architecture, not just model quality.
- Optimize KV cache before scaling GPUs
- Separate prefill and decode workloads
- Use cache-aware routing for latency
DeepLearningAI · 2026-06-03 · 5,968 views · 🔥 373/day
Running open-source LLMs cheaply is mostly a memory problem: model weights and KV cache fight for the same scarce budget. The fix is practical—quantize the model, serve with vLLM using tricks like PagedAttention and prefix caching, then benchmark under realistic load. That matters because deployment decisions live in the tradeoff between accuracy, latency, and cost.
- Quantize weights before scaling inference.
- Use prefix caching to cut repeat costs.
- Benchmark with realistic traffic, not demos.
Tonbi's AI Garage · 2026-06-03 · 8,405 views · 🔥 525/day
Skip the wrappers: raw llama.cpp gives you the real control surface for local models, from sampling and schema-locked JSON to KV-cache, GPU offload, and OpenAI-compatible serving. That matters because once you understand the engine underneath Ollama and LM Studio, you can tune quality, speed, and integrations instead of accepting someone else’s defaults.
- Run llama-server for a local OpenAI-compatible endpoint.
- Use JSON schemas for reliable structured output.
- Tune GPU layers, cache, and sampling together.