DeepLearningAI · 2026-06-03 · 5,536 views · ๐ฅ 461/day
Running open-source LLMs well is mostly a memory problem: model weights fight the KV cache, and vLLM wins back headroom with quantization, PagedAttention, and prefix caching. The useful bit is the full workflow on a real Qwen model, from compression to serving to benchmarking, so you can make sharper speed, accuracy, and cost tradeoffs before deployment.
- Quantize weights before scaling inference.
- Use vLLM caching to cut latency.
- Benchmark realistic traffic, not toy prompts.
Tonbi's AI Garage · 2026-06-03 · 8,113 views · ๐ฅ 676/day
Stop letting wrappers hide the controls that actually shape local model behavior: llama.cpp is the engine, and running it directly unlocks the knobs that matter. The real advantage is precise control over sampling, structured output, performance, and API serving from one lightweight stack. That matters if you want faster tuning, cleaner automation, and fewer black-box limitations.
- Run llama.cpp directly for full control.
- Use schemas for reliable JSON outputs.
- Expose llama-server as local OpenAI API.
Codacus · 2026-05-19 · 22,054 views · ๐ฅ 816/day
A new llama.cpp merge makes local inference dramatically faster without changing your hardware: Multi-Token Prediction delivered 65% on a MacBook Pro and 23% on a budget GPU. The gains depend on whether youโre CPU- or GPU-bound, with draft acceptance shaping how much speed you actually keep. If you run self-hosted LLMs, this is one of the rare free performance wins worth testing now.
- Enable MTP and benchmark your current workload
- Compare gains on CPU-bound versus GPU-bound runs
- Tune draft acceptance and speculative decoding flags