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COMPUTING
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CPU Role in Agentic Systems: Inference Orchestration
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In agentic systems, CPUs do two things: orchestrate inference and run everything around it: LLVM compilation, vector DB queries, tool calls.
— SambaNova (@SambaNovaAI) 29 mai 2026
Faster execution at each step = shorter agent loop. That's why Xeon 6 + RDU is the full stack, not just the accelerator. pic.twitter.com/rwYYigDC2EIn agentic systems, CPUs do two things: orchestrate inference and run everything around it: LLVM compilation, vector DB queries, tool calls. Faster execution at each step = shorter agent loop. That's why Xeon 6 + RDU is the full stack, not just the accelerator.
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MiniMax M2.7 deployed on SambaCloud to improve coding-agent performance
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MiniMax M2.7 just landed on @SambaNovaAI
's SambaCloud. If you build with coding agents, you know a smart model is only half the battle! Sluggish response times and strict rate limits during iterative, multi-turn coding sessions completely break your momentum. SambaCloud -
AI Performance as System-Level Enterprise Challenge
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The enterprise takeaway is simple: AI performance is now a system-level challenge. The winners will optimize chips, memory, interconnects, software, and architecture together. Less latency means faster intelligence.
Less movement means lower cost.
Less waste means AI that can -
LogicFolding: 3D Chip Architecture for AI Inference Optimization
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One concept that makes this practical is LogicFolding. Traditional chips spread logic across a flat surface. LogicFolding brings related logic closer together by moving toward more 3D structures. Less distance means less delay. And in AI workloads, small delays compound fast.
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Tau Scaling Law: Beyond Chip Size in AI Systems
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This is the idea behind Tau Scaling Law, also known among peers as Her’s Law. Instead of asking only, “How small can the chip get?” We now have to ask: → Where is time being lost?
→ Where is data waiting?
→ Where are signals traveling too far?
→ Where is the system -
Edge-Cloud Hybrid Architecture for AI Development
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The architecture is typically hybrid: edge handles latency-sensitive control, cloud platforms handle analytics and AI development at scale.
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General Compute Builds Efficient AI Inference Cloud Infrastructure
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@TimFernholz at @TechCrunch breaks down how General Compute is building its inference cloud with SambaNova, and why faster, more efficient inference infrastructure is becoming critical for the next wave of AI. Featuring insights from General Compute CEO @FPuklowski and CTO
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JAX GPU scaling and pipeline parallelism for training
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Yes. It’s not that we’ve discovered some magic bullet, but rather that JAX, or at least the open source version of it, is mostly optimized for small to medium-sized training runs on Google TPUs, whereas we need to massive training runs on Nvidia GPUs. Pipeline parallelism is