Guides and benchmarks for the latest open-source and proprietary models, with practical tips for running them in production.
A practical map of the open-source LLM ecosystem in 2025: the leading model families, how they stack up by size and task, what the licenses actually let you do, and how to pick one for production.
Faster-Whisper reimplements OpenAI's Whisper on CTranslate2 with INT8 inference, running several times faster at the same accuracy. Here is how it works, how streaming differs from batch transcription, and how it fits into a real-time STT to LLM to TTS voice pipeline.
QwQ-32B is a 32-billion-parameter open-weight reasoning model from the Qwen team that competes with much larger reasoning models. Here is how it works, how it compares to o1, o1-mini, and DeepSeek R1, and what its long reasoning traces mean when you serve it in production.
A practical walkthrough for fine-tuning Llama 4: when to do it, how to prepare data, and working LoRA, QLoRA, and full fine-tune code, plus evaluation and deployment.
A close look at Qwen3-Coder: how it scores on HumanEval, MBPP, and SWE-bench, how it compares to Code Llama and DeepSeek Coder, and how to wire it into your editor and agents.
A practical guide to running Llama 4 on GeneralCompute: the model variants, what hardware they need, how to make your first API call, and how to tune requests for speed and cost.
DeepSeek R1 is an open-weight reasoning model trained mostly through reinforcement learning. Here is how its architecture and training work, how it compares to GPT-4 class models, Claude, and Llama, and what its reasoning style means for inference.