DeepLearningAI · 2026-06-03 · 5,756 views · π₯ 411/day
Serving open-source LLMs cheaply is mostly a memory fight: model weights versus a fast-growing KV cache. The practical win is combining quantization with vLLM features like PagedAttention and prefix caching, then benchmarking under realistic load. That gives you a repeatable way to trade accuracy, latency, and cost before production bites back.
- Quantize weights before scaling inference.
- Use prefix caching to cut repeat costs.
- Benchmark realistic traffic, not toy prompts.
Tonbi's AI Garage · 2026-06-03 · 8,237 views · π₯ 588/day
Stop treating local AI apps as the engine; the real control lives in llama.cpp. Running it directly unlocks the knobs wrappers hide: structured JSON schemas, OpenAI-compatible local APIs, cache and context tuning, and hardware-specific speed controls. That matters if you want predictable outputs, better performance, and less vendor-shaped friction.
- Run llama.cpp directly for full parameter control.
- Use schemas for reliable structured JSON output.
- Expose llama-server API to connect external apps.
Codacus · 2026-05-19 · 22,290 views · π₯ 768/day
llama.cppβs new Multi-Token Prediction can deliver a real 65% local inference speedup, but only when your hardware can actually exploit it. On dense Metal workloads it flies, while GPU-bound MoE setups gain less, so test MTP on your own stack before assuming headline numbers.
- Enable MTP and benchmark your exact hardware.
- Expect bigger gains on dense, CPU-bound workloads.
- Tune speculative decoding flags for your model.