Nvidia BlueField-4 STX adds a context memory layer to storage to close the agentic AI throughput gap
When an AI agent loses context mid-task because traditional storage can't keep pace with inference, it is not a model problem — it is a storage problem. At GTC 2026, Nvidia announced BlueField-4 STX, a modular reference architecture that inserts a dedicated context memory layer between GPUs and traditional storage, claiming 5x the token throughput, 4x the energy efficiency and 2x the data ingestion speed of conventional CPU-based storage.The bottleneck STX targets is key-value cache data. KV cache is the stored record of what a model has already processed — the intermediate calculations an LLM saves so it does not have to recompute attention across the entire context on every inference step. It is what allows an agent to maintain coherent working memory across sessions, tool calls [...]
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Nvidia launches enterprise AI agent platform with Adobe, Salesforce, SAP among 17 adopters at GTC 2026
Jensen Huang walked onto the GTC stage Monday wearing his trademark leather jacket and carrying, as it turned out, the blueprints for a new kind of monopoly.The Nvidia CEO unveiled the Agent Toolkit, [...]
Nvidia's agentic AI stack is the first major platform to ship with security at launch, but governance gaps remain
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Nvidia introduces Vera Rubin, a seven-chip AI platform with OpenAI, Anthropic and Meta on board
Nvidia on Monday took the wraps off Vera Rubin, a sweeping new computing platform built from seven chips now in full production — and backed by an extraordinary lineup of customers that includes Ant [...]
Ford's F-150 Lightning STX replaces the XLT while boosting range and power
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Context architecture is replacing RAG as agentic AI pushes enterprise retrieval to its limits
Redis built its name as the caching layer that kept web applications from collapsing under load. The problem it is targeting now has the same structure but is harder to solve: production AI agents fai [...]
GAM takes aim at “context rot”: A dual-agent memory architecture that outperforms long-context LLMs
For all their superhuman power, today’s AI models suffer from a surprisingly human flaw: They forget. Give an AI assistant a sprawling conversation, a multi-step reasoning task or a project spanning [...]
AI agents keep giving confident wrong answers. The context layer is enterprise AI's next production problem.
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A 0.12% parameter add-on gives AI agents the working memory RAG can't
AI agents forget. Every time a coding assistant loses track of a debugging thread, or a data analysis agent re-ingests the same context it already processed, the team pays in latency, token costs, and [...]