Videos

Self-hosted LLM inference clusters without admission control are vulnerable to overload from coding agents. The key...

Claude Sonnet 3.5 produced a 2.5-bit quantized Qwen 27B model that fits in 8GB and beats a human-made quant on...

Sam Altman's claim that local AI wastes memory holds up on efficiency grounds: sharing model weights across users on...

The real story here is that the AI-driven GPU price surge headlines are mostly about high-end cards: in eBay sold...

DeepSeek's cheap repeated-prompt pricing comes from an on-disk KV cache: the engine stores prompt-reading notes on...

Osmantic's ODS demo puts a 180-billion-parameter Qwen model on a roughly $7,000 unified-memory Windows laptop and...

The claimed 15-25% speed advantage for Linux over Windows in local AI is misleading: when models fit in VRAM,...

Microsoft's Decision 1 is a decision model that never generates words: it reads the input and candidate options in...

Speculative decoding can speed up LLM inference by 1.6x on structured tasks like code or JSON generation, but offers...

Inference engines are architecturally split into a host-side scheduler (the bottleneck) and GPU-side model runners,...

vLLM's paged attention mechanism, which treats KV cache like virtual memory pages, directly solved the 60-80% memory...

The OpenRouter-Stripe acquisition is fundamentally a security story: the token economy is projected to reach $10...

Fitting a model on your GPU is not enough; you must also account for KV cache and runtime overhead. For most...

Turboquant, which uses polar coordinates to quantize the KV cache to four bits, has a fatal flaw for data center...

Speculative decoding is a lossless inference acceleration technique that uses a small draft model to propose...