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Latest from Baidu Intelligent Cloud Tech Hub

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Baidu Intelligent Cloud Tech Hub

Jul 30, 2026 · Artificial Intelligence

Boosting Cosmos‑3 Training Throughput by 35% on the Same Budget with Heterogeneous GPU Clusters

By decoupling VAE encoding from the main training GPU, redesigning the data flow into a fully asynchronous pipeline, and applying dynamic scheduling, multi‑level caching, and custom operators, the Cosmos3‑Nano‑Policy‑DROID system achieves a 2.1× overall throughput with only 1.5× the hardware cost, delivering roughly 35% more training throughput for the same budget.

AI InfraGPU clusterVAE Encoding

0 likes · 18 min read

Boosting Cosmos‑3 Training Throughput by 35% on the Same Budget with Heterogeneous GPU Clusters

Baidu Intelligent Cloud Tech Hub

Jul 20, 2026 · Cloud Native

Redesigning Parallel File Storage for AI Production: Introducing Baidu PFS L3

As AI moves from isolated model training to full‑scale production with mixed workloads, Baidu's new PFS L3 offers elastic, cloud‑native parallel file storage that delivers tens of GB/s throughput, millions of IOPS, sub‑millisecond latency, and automated data lifecycle management to meet the evolving demands of modern AI platforms.

AI infrastructureBaidu CloudData Lifecycle Management

0 likes · 12 min read

Redesigning Parallel File Storage for AI Production: Introducing Baidu PFS L3

Baidu Intelligent Cloud Tech Hub

Jun 29, 2026 · Artificial Intelligence

How to Maximize Cosmos‑3 Training Throughput Without NVLink Using AI Infra Optimizations

By applying systematic AI Infra engineering—optimizing data loading, I/O pipelines, activation checkpointing, torch.compile, and multi‑node scaling—the Cosmos‑3‑Nano‑Policy‑DROID model achieved an 89× faster startup, 99.3% higher single‑node throughput, 0.42 MFU, and 98.3% scaling efficiency across 12 nodes, all without NVLink.

AI InfraActivation CheckpointingCosmos 3

0 likes · 13 min read

How to Maximize Cosmos‑3 Training Throughput Without NVLink Using AI Infra Optimizations

Baidu Intelligent Cloud Tech Hub

Jun 17, 2026 · Artificial Intelligence

The Multimodal Model Battlefield Is Going Rogue – LoongForge’s ‘Dark Arts’ Framework

Facing mounting challenges of heterogeneous models, data, and hardware in multimodal training, Baidu’s open‑source LoongForge framework unifies LLM, VLM, VLA and diffusion workloads, delivering 1.15‑2.31× speedups and over 5× gains for DSA models while scaling linearly across thousands of GPUs and Kunlun XPU cards.

GPUKunlun XPULarge Language Models

0 likes · 8 min read

The Multimodal Model Battlefield Is Going Rogue – LoongForge’s ‘Dark Arts’ Framework

Baidu Intelligent Cloud Tech Hub

Jun 10, 2026 · Artificial Intelligence

LU‑KV Sets New SOTA at ICML 2026 by Redefining KV Cache Eviction

A joint effort by Baidu Baige and Fudan University introduces the LU‑KV framework, which treats KV‑cache budget allocation as a global combinatorial optimization problem, achieving only 0.52% relative performance loss at 80% compression and establishing a new efficiency‑accuracy SOTA on LongBench.

Cache EvictionEfficiencyICML 2026

0 likes · 5 min read

LU‑KV Sets New SOTA at ICML 2026 by Redefining KV Cache Eviction

Baidu Intelligent Cloud Tech Hub

Jun 2, 2026 · Artificial Intelligence

Halving Training Time: LoongForge Full‑Stack Optimizations Boost GR00T N1.6 Throughput 2.3×

LoongForge applies system‑level optimizations—async data prefetch, fine‑grained communication‑compute overlap via a Megatron distributed optimizer, and per‑microbatch CUDA Graph scheduling—to the GR00T N1.6 Vision‑Language‑Action model, delivering up to 2.3× higher training throughput and a 56.6% reduction in overall training time on an 8×A800 cluster.

CUDA GraphGR00T N1.6LoongForge

0 likes · 14 min read

Halving Training Time: LoongForge Full‑Stack Optimizations Boost GR00T N1.6 Throughput 2.3×

Baidu Intelligent Cloud Tech Hub

Jun 1, 2026 · Cloud Computing

Cut Migration Time by 60%: Baidu Cloud Deploys Intel Xeon 6 QAT‑Accelerated Live VM Migration

The article analyzes the challenges of large‑scale live VM migration, introduces Intel Xeon 6 CPU‑integrated QAT hardware acceleration, compares pre‑ and post‑QAT workflows, and reports a 60% reduction in migration time, 20% CPU savings, and sub‑10 ms downtime in Baidu Smart Cloud production.

Cloud ComputingIntel QATVM live migration

0 likes · 10 min read

Cut Migration Time by 60%: Baidu Cloud Deploys Intel Xeon 6 QAT‑Accelerated Live VM Migration

Baidu Intelligent Cloud Tech Hub

May 29, 2026 · Industry Insights

How Baidu’s Hanhai U Series Cuts 3 Million Yuan Cost for 10 MW High‑Density AI Data Centers

The article analyzes the power‑supply challenges of high‑density AI data centers, compares traditional UPS and 800 V DC architectures, and shows how Baidu’s Hanhai U series redesign delivers precise capacity matching, up to 2.5× higher power density, 55% space reduction and up to 15% cost savings.

AI ComputeBaiduHanhai U series

0 likes · 11 min read

How Baidu’s Hanhai U Series Cuts 3 Million Yuan Cost for 10 MW High‑Density AI Data Centers

Baidu Intelligent Cloud Tech Hub

May 27, 2026 · Artificial Intelligence

Optimizing Large Model Inference Architecture for the Agent Era: Engineering Practices and Challenges

The article analyzes the architectural challenges of large‑model inference in the Agent era—such as memory‑intensive MLA structures, MoE communication overhead, exploding KV‑Cache size, and tool‑call accuracy—and presents a series of engineering solutions including hierarchical KV‑Cache pooling, sequence parallelism, offloading strategies, and chip‑level adaptations to achieve higher throughput and lower token costs.

AI InfraAgentDeepSeek

0 likes · 15 min read

Optimizing Large Model Inference Architecture for the Agent Era: Engineering Practices and Challenges

Baidu Intelligent Cloud Tech Hub

May 26, 2026 · Operations

When CPUs Hide GPU Bottlenecks: How Btune 2.0 Automates Latency Analysis to Uncover Performance Issues

The article presents a real‑world migration case where a CPU‑XPU bottleneck limited inference QPS, explains how Btune 2.0’s new latency‑focused diagnostics pinpointed a kernel lock contention in the halolet component, and shows the AI Agent’s automated, cross‑process analysis that restored performance and reduced cost.

AI infrastructureCPU-GPU bottleneckCross-process analysis

0 likes · 11 min read

When CPUs Hide GPU Bottlenecks: How Btune 2.0 Automates Latency Analysis to Uncover Performance Issues