Alexa's Input (AI) · 2026-06-01 · 24,435 views · π₯ 1,221/day
AI inference stopped being a model-serving problem and became a distributed systems fight over memory, routing, and latency. vLLM and PagedAttention reshaped inference economics by making KV cache efficiency central, while llm-d points toward orchestration that separates prefill, decode, and traffic control. That matters because long-context agents and enterprise workloads break fast without cache-aware, distributed serving.
- Optimize KV cache before scaling GPUs.
- Separate prefill and decode paths.
- Use cache-aware routing and flow control.
DeepLearningAI · 2026-06-03 · 6,073 views · π₯ 337/day
Open-source LLM serving lives or dies on memory: model weights fight KV cache, and vLLM wins by managing both smarter. Quantize the model, serve with PagedAttention and prefix caching, then benchmark under real traffic to find the speed-cost-accuracy line that actually holds in production.
- Quantize weights before scaling inference.
- Use prefix caching to cut repeated latency.
- Benchmark realistic traffic, not toy prompts.
Tonbi's AI Garage · 2026-06-03 · 8,493 views · π₯ 471/day
Skip the wrappers: raw llama.cpp gives you direct control over the knobs that actually change local-model behavior, from sampling and structured JSON to KV cache and GPU offload. That matters if you want reproducible output, OpenAI-compatible local APIs, and better performance tuning instead of whatever Ollama or LM Studio expose.
- Run llama-server for local OpenAI-compatible APIs.
- Use schemas for reliable structured JSON output.
- Tune GPU layers, cache, and sampling directly.