Alexa's Input (AI) · 2026-06-01 · 20,542 views · 🔥 1,369/day
AI inference stopped being a model-serving problem and became a distributed systems fight. Rob Shaw breaks down how vLLM, PagedAttention, and llm-d make KV cache, routing, and prefill/decode separation the new levers for throughput and latency. That matters because real-world agents and enterprise workloads now live or die on inference orchestration, not just model quality.
- Measure KV cache hit rates first.
- Separate prefill and decode paths.
- Route requests with cache awareness.
DeepLearningAI · 2026-06-03 · 5,580 views · 🔥 429/day
Serving open-source LLMs cheaply is mostly a memory problem: model weights fight KV cache for space. The fix is pragmatic—quantize the model, then use vLLM features like PagedAttention and prefix caching to preserve throughput under realistic load. That matters because better memory efficiency directly improves latency, capacity, and cost.
- Quantize weights before scaling inference.
- Use prefix caching for repeated prompts.
- Benchmark under realistic multi-user traffic.
Tonbi's AI Garage · 2026-06-03 · 8,133 views · 🔥 625/day
Skip the wrapper and drive llama.cpp directly if you want real control over local models. The payoff is access to the knobs that actually matter—sampling, structured output, tool calling, context, and performance tuning—plus a local OpenAI-compatible API. That means better reliability, cleaner integrations, and fewer black-box limitations from apps like Ollama or LM Studio.
- Run llama.cpp directly for full parameter control.
- Use llama-server for OpenAI-compatible local APIs.
- Enforce JSON schemas with grammar-constrained output.