It actually does matter here — number of GPUs in a system is almost always a power of two, and to maximize your utilization you will want that to evenly divide into the number of tasks.
SYSTEMS
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AI Adoption Challenges in Socially Constructed Organizations
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A challenge with AI adoption is that organizations are not built to a Grand Plan where AI can just be slotted in, but rather socially constructed, random & in flux Here's an anecdote from a paper on how a process re-engineering effort led to revelations that drove people insane.
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NVIDIA Grace Blackwell Systems Launch at VivaTech 2025
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GTC Highlights: Lighting Up Europe
— NVIDIA (@nvidia) 1 juillet 2025
GTC Paris at VivaTech 2025 spotlighted how NVIDIA and partners are building the future of AI—from sovereign infrastructure to agentic systems.
Jensen Huang’s keynote introduced major advances like Grace Blackwell systems in full production and… pic.twitter.com/Y9294Q3druGTC Highlights: Lighting Up Europe GTC Paris at VivaTech 2025 spotlighted how NVIDIA and partners are building the future of AI—from sovereign infrastructure to agentic systems. Jensen Huang’s keynote introduced major advances like Grace Blackwell systems in full production and
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Autonomous AI Agents for Network-Level Disruption Management
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Think of the dozens of disruptions and surprises that occur across networks and enterprises every day. Please explore with me (
@TerenceLeungSF
) about specialized, #autonomous #AI agents that are driving incredible results at the network level. When unexpected events occur, AI -
Test-Time Scaling: Deep Reasoning During AI Inference
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#3: Test-time scaling uses extra compute during inference to reason through complex problems—like a professional thinking deeply before making a big decision.
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Autonomous AI Systems at Scale: Project44 Architecture Analysis
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The real innovation lies in designing systems that can act on data with autonomy and context—my analysis on project44 shows how intelligence at scale is no longer an ambition, but a working architecture.
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Draft Model Pruning Achieves 43% Fewer MACs with Strong Performance
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The results: – 1.59× higher Mean Accepted Length (MAL) than layer-pruned draft models
– 43.87% fewer MACs (Multiply-Accumulate operations) than dense draft models
– Only 8.36% reduction in MAL vs. dense models — a strong tradeoff for efficiency -
SD² Enhances Draft Token Acceptance Reducing MACs
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SD² systematically enhances draft token acceptance rates while significantly reducing Multiply-Accumulate operations (MACs), even in the Universal Assisted Generation (UAG) setting, where draft and target models originate from different model families.
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Microsoft AI System Simulates Physician Panel for Diagnosis
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Microsoft AI built MAI-DxO to simulate a virtual panel of physicians with different approaches collaborating to find a diagnosis on each case. They also included the ability to set a budget to avoid infinite testing (higher costs, longer wait times, etc.).
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Security challenges in MCP prompt manipulation
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Oui ca dépend du cas d'usage. Parce qu'il peut y avoir aussi des problèmes de sécurité encore coté MCP sur de la manipulation de promptS.