#llamacpp

Unlock local AI inference speedups by optimizing legacy GPUs, custom kernels like megakernel, and DIY cooling for mining rigs—boost your model performance.

Explore files faster with DigUp, the new macOS app that uses DeepMind’s EmbeddingGemma 2 to search text, images, audio, and video in seconds—locate exact clauses or moments locally, no internet needed.

Explore Tencent’s Youtu‑Parsing‑Omni: a 5B‑parameter omni‑modal model that turns text, images, audio, and video into structured JSON with top benchmark performance.

Google’s open‑source ML Drift lets developers run high‑performance on‑device AI on any GPU—supporting OpenGL ES, Metal, OpenCL, and WebGPU for real‑time inference.

Maximize LLM inference speed with high‑VRAM rigs and custom vLLM forks—discover why Qwen3.8‑Flash‑Next outperforms GLM‑5.3‑Flash in raw throughput yet GLM excels in task latency.

Qwen Flash Next offers blazing speed, but users report instability on long, complex tasks—discover how to balance speed vs. reasoning for best results.

New llama.cpp GPU cache speeds Mixture‑of‑Experts models on low‑VRAM GPUs, boosting local AI performance and keeping open‑source deployment competitive.

Bev: a Qwen3.5-9B model that purposefully picks wrong answers with 96% confidence, designed to test AI safety pipelines for confidently incorrect behavior.

LLM benchmarks show GLM 5.3 Flash & Qwen3.8-Flash-Next run 68.8 tok/s on Apple M5 Ultra and 44‑59 tok/s on AMD Strix Halo—high‑performance inference on consumer hardware.

Discover how llama.cpp 0.6.0’s new Multi‑Token Prediction boosts Qwen4Exp inference and why specialized forks claim 3× speed gains—what it means for AI‑powered dev tools.