DeepLearningAI · 2026-06-03 · 5,904 views · 🔥 393/day
Cheap LLM serving is mostly a memory problem: model weights fight the KV cache, so the win comes from quantization plus vLLM tricks like PagedAttention and prefix caching. This walks through compressing Qwen, serving it with vLLM, and benchmarking the result so you can choose the least painful tradeoff between accuracy, latency, and cost.
- Quantize weights before scaling traffic.
- Use prefix caching to cut latency.
- Benchmark cost, speed, and accuracy.
Tonbi's AI Garage · 2026-06-03 · 8,333 views · 🔥 555/day
Stop treating Ollama and LM Studio as the product; they’re mostly skins over llama.cpp. Running llama.cpp directly gives you the real controls: structured output, tool calling, context tuning, GPU offload, and an OpenAI-compatible local API. That matters if you want better performance, tighter reliability, and fewer black-box limits in local AI workflows.
- Run llama.cpp directly for full control.
- Use llama-server for local API access.
- Tune sampling, context, and GPU layers.
Alexa's Input (AI) · 2026-06-01 · 23,807 views · 🔥 1,400/day
AI inference stopped being a model-serving problem and became a distributed systems fight over memory, routing, and latency. Rob Shaw breaks down how vLLM, PagedAttention, and llm-d changed the economics by making KV cache management and coordinated scheduling first-class infrastructure concerns. That matters because long-context agents and enterprise deployments now live or die on inference efficiency, not model quality alone.
- Optimize KV cache before scaling clusters.
- Separate prefill and decode workloads.
- Use cache-aware routing to cut latency.