●NANOLITE — Nano Banana 2 Lite is here: Google's fastest and most cost-efficient Gemini Image model, made for running lightweight image generation cheaply●OMNIFLASH — Gemini Omni Flash is in public preview, a natively multimodal model that lets enterprises and developers build custom, dynamic video workflows●AGENTS — Managed Agents expand with background: true for async server-side runs and polling, remote MCP server integration, and refreshing credentials across interactions●MEMORY — The Memory Bank IngestEvents API is generally available, decoupling event ingestion from memory generation so you can stream content continuously●THROUGHPUT — Provisioned Throughput now lets you submit up to seven pending orders for the same model and region●DEPRECATE — Image generation models shut down on August 17, and the Grok 4.1 family on the Gemini Enterprise Agent Platform on August 20●NANOLITE — Nano Banana 2 Lite is here: Google's fastest and most cost-efficient Gemini Image model, made for running lightweight image generation cheaply●OMNIFLASH — Gemini Omni Flash is in public preview, a natively multimodal model that lets enterprises and developers build custom, dynamic video workflows●AGENTS — Managed Agents expand with background: true for async server-side runs and polling, remote MCP server integration, and refreshing credentials across interactions●MEMORY — The Memory Bank IngestEvents API is generally available, decoupling event ingestion from memory generation so you can stream content continuously●THROUGHPUT — Provisioned Throughput now lets you submit up to seven pending orders for the same model and region●DEPRECATE — Image generation models shut down on August 17, and the Grok 4.1 family on the Gemini Enterprise Agent Platform on August 20

TAG

turboquant

2 articles

← Back to all tags

gemini² rag¹ vector-search¹ embedding¹ performance-optimization¹ polarquant¹ qjl¹ quantization¹ kv-cache¹ deep-dive¹

⬡ Gemini Advanced/2026-03-28Intermediate

Applying TurboQuant to RAG and Vector Search — New Uses for KV Cache Compression

Google's TurboQuant compression technology extends beyond LLM inference to RAG pipeline vector databases. Learn how embedding vector compression can improve memory efficiency, search speed, and scalability for large-scale RAG systems.

⬡ Gemini Advanced/2026-03-27Advanced

TurboQuant Technical Deep Dive: PolarQuant and QJL Algorithms Explained

Explore the mathematics behind TurboQuant's PolarQuant and QJL algorithms at the formula level. Compare with GPTQ, AWQ, and SqueezeLLM. Understand implementation details and TurboQuant's transformative potential for Gemini.