scalability
4 published articles
Research
Shared memory helps AI agents, but only until you hit five of them
Researchers shared replay buffers across actor-critic agents on parameterized action tasks. GAC jumped in performance, but SAC and TQC only crept forward. Beyond five agents, the computational cost climbs with no meaningful return. The paper offers a practical boundary for how far shared-experience methods can stretch before they stall.
2026-07-27
Infrastructure deep-dive
Nous Research's MoE field notes: what actually happens when 1 trillion parameters hit 1024 GPUs
Nous Research publishes field notes on scaling MoE expert parallelism with DeepEP. The report details throughput, configuration trade-offs, and bottlenecks from pretraining a 1T-parameter MoE model, offering practical deployment insights for large-scale distributed training.
2026-07-19
Memory Management
mimalloc, Microsoft's tiny memory workhorse, is quietly powering AI at scale
Microsoft Research's mimalloc memory allocator, designed for high concurrency and large memory scales, is quietly powering AI and cloud services at scale. Its thread-local design with thousands of free lists minimizes contention and delivers both speed and memory efficiency.
2026-07-03
Synthetic Data
Synthetic data generation just ditched its weakest link: the boss
Matrix is a decentralized framework that uses serialized messages passed through distributed queues for multi-agent synthetic data generation. By eliminating the central orchestrator, it achieves 2 to 15 times higher throughput on identical hardware.
2026-06-06