LLM Quantization Explained: GPTQ, AWQ, GGUF, and When Each One Wins
Serving a 7B model in fp16 takes roughly 14 GB of VRAM. A 70B model takes around 140 GB —
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Tag: Quantization
Training-Inference Mismatch: Why Your LLM Reinforcement Learning Is Optimizing the Wrong Policy
Reinforcement learning has become the defining ingredient of modern LLM post-training. GRPO, PPO, and their variants drive the reasoning capabilities
vLLM v0.23.0: Model Runner V2, Multi-Tier KV Offloading, and the Growing Rust Frontend
The vLLM v0.23.0 release landed last week with 408 commits from 200 contributors, and it packs several changes that directly
The AI Glossary: Every Term You Need to Know in 2025
Entering the AI space feels like learning a new language. Everyone throws around RAG, RLHF, GGUF, MoE, MCP like you’re
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