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Graph architecture replaces transformer attention, extrapolating to 4 million tokens in linear time
GTFormer proposes replacing Transformer self-attention with a hierarchical graph neural network, achieving zero-shot length ...
NVIDIA’s Vera Rubin Platform marks a decisive shift toward inference workloads and an increasing need for significantly cheaper token generation. Overhauling Blackwell architecture, what challenges ...
A newly published hybrid index structure combines an adaptive radix tree with a hash-based skip list to cut deep-key lookup ...
At OCP 2026, Samsung and SK hynix both detailed strategies for addressing the memory demands of agentic AI’s key-value cache workloads, with coverage framing the announcements as further evidence that ...
Khawaja Shams shares a distributed systems perspective on scaling LLM inference stacks efficiently. He discusses practical strategies for optimizing GPU utilization, managing KV cache memory cliffs, ...
Micron expects higher HBM pricing in calendar 2027 to narrow its margin gap with conventional DRAM. But the long-term test is ...
The 5th GMIF Innovation Summit (Global Memory Innovation Forum 2026, GMIF2026), co-hosted by the Shenzhen Memory Industry ...
What should we do about this matter?When executives ask me that, I end up answering.Even though I said I would leave it to ...
ST's HELIX research program with NUS will explore how memory, processing and software can be designed together for real-time ...
I recently read an article by marfin (@marfinxx) titled "Autonomous Agent Architecture: Unifying Context Engineering and ...
As AI workloads evolve toward inference and agentic AI, the criteria for system performance are also changing. Computing ...
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