Google's Gemini 3.5 Flash hit general availability with 4x faster inference than competing frontier models, a 1M token context window, and $1.50/$9 pricing. It outperforms Gemini 3.1 Pro on coding and agentic benchmarks. Here's the honest assessment of what this means.
Google's TurboQuant research reduces KV cache memory overhead by ~100x using a two-step algorithm combining PolarQuant vector rotation and Johnson-Lindenstrauss compression. This could make 2M-token context models economically feasible for many more teams.
An 8B model that fits in 1GB RAM, runs on your iPhone, and stays competitive with full-precision models. PrismML's Bonsai changes the math on edge AI deployment. Here's the technical breakdown.
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