This or That with Takara AI
summary
Takara AI claims the fastest CPU embedding models, self‑host inference under 1 ms and over‑the‑wire ~80 ms versus ~600 ms to OpenAI, enabling billions of tokens per hour. Embeddings sit in CockroachDB and replicate globally; deployment to 21 AWS regions avoids GPU scarcity and cuts infra cost to cents/hour. A deployed code‑agent replaces expensive LM searches, shaving about 40% off code‑generation bills. Demonstrated workflow runs on commodity CPUs, lowers latency for regionally colocated vector search, and materially reduces OPEX and time to value.
AI-written summary · about
chapters (16)
- 0:00Podcast introduction
- 0:25Takara AI and embeddings
- 1:30CPU embeddings speed and cost
- 2:31Why GPUs aren’t needed
- 3:37Deployment options and entry
- 4:40Cost savings with Takara
- 5:43Global deployment and data stores
- 6:48CPU deployment flexibility
- 7:53Minimizing latency and improving UX
- 8:53Speed’s impact on AI agents
- 9:35Coding agents and vector search
- 10:57Savings with Takara’s agent
- 11:39Vector vs operational database
- 12:42DPU-first vs CPU-first
- 13:23Build or buy AI platform
- 14:28Pitch deck vs live demo
related talks
14mReliable Agentic AI, the Open Way
43mBird's Eye View of Open-Source AI Infrastructure
23mThe Race to the Bottom: Low Latency in the age of the Transformer
45mScalable Open AI GPT Applications in the Cloud
17mEnable Generative AI Everywhere with Ubiquitous Hardware and Open Software
02h 35mBringing GenAI to the Modern Enterprise. A production use-case. In Serverless Java!
19mReal-Time AI: Low Latency Solutions for Interactive Applications
52mSimplifying the adoption of generative AI for enterprises