Pithy Cyborg | AI News Made Simple
Pithy Cyborg | AI News Made Simple
Most RAG pipelines fail not because the technology is wrong but because they were validated on the wrong data, against the wrong questions, by the people who built them. The retrieval looks good in …
Continue Reading about Why Most RAG Pipelines Fail Before They Ever Hit Production →
Yes, with a precise definition of "production." A $600 GPU in 2026 means roughly 16 to 24GB of VRAM, which is enough to run a quantized 14B to 32B parameter model at inference speeds that feel …
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vLLM's benchmarks are real. Its memory requirements in production are significantly higher than those benchmarks imply, and the gap is not disclosed prominently anywhere in the official documentation. …
Continue Reading about What vLLM Won’t Tell You About Memory Overhead →
Slow local LLM inference is almost never the model's fault. It is almost always a configuration problem sitting between your hardware and the model that nobody in the setup tutorials mentions. The …
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Not destroy. Degrade selectively, in ways that are predictable once you know where to look. 4-bit quantization compresses Llama 4's weights from 16-bit floats down to 4-bit integers, cutting memory …
Continue Reading about Does 4-Bit Quantization Actually Destroy Llama 4 Reasoning? →
Switching from ChatGPT to a local Llama 4 deployment stops your prompts from reaching Meta and OpenAI. It does not stop your data from leaking. Local LLM deployments have at least five active …
Continue Reading about Why Running Llama 4 Locally Still Leaks More Than You Think →
