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Latest from Baidu Intelligent Cloud Tech Hub

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Baidu Intelligent Cloud Tech Hub

Jan 16, 2026 · Artificial Intelligence

How LoongFlow Empowers Expert‑Level AI Agents to Surpass Human Mathematicians

LoongFlow is an open‑source AI agent framework that combines a Plan‑Execute‑Summarize (PES) paradigm with a Hybrid Evolutionary Memory system to enable agents to perform long‑range, complex reasoning, achieving record‑breaking results on mathematical challenges and real‑world ML benchmarks while dramatically improving efficiency.

LoongFlowPES paradigmevolutionary optimization

0 likes · 15 min read

How LoongFlow Empowers Expert‑Level AI Agents to Surpass Human Mathematicians

Baidu Intelligent Cloud Tech Hub

Jan 12, 2026 · Artificial Intelligence

How to Reduce Large‑Model Inference Cold‑Start to Seconds with vLLM Optimizations

This article details how Baidu Cloud's hybrid‑cloud team leveraged the vLLM framework to cut the cold‑start time of massive models like Qwen3‑235B‑A22B from minutes to a few seconds through accelerated weight loading, CUDA‑graph capture postponement, cross‑instance state reuse, fork‑based process startup, and guard‑instance pre‑warming techniques.

CUDA Graphcold-start optimizationlarge-model inference

0 likes · 16 min read

How to Reduce Large‑Model Inference Cold‑Start to Seconds with vLLM Optimizations

Baidu Intelligent Cloud Tech Hub

Jan 6, 2026 · Operations

How vLLM‑Kunlun Plugin Enabled Two‑Day Adaptation of MiMo Flash V2 on Kunlun P800 XPU

In just two days, Baidu Baige and Kunlun's engineers extended the vLLM‑Kunlun Plugin to overcome asymmetric KV dimensions and integrate SWA+Sink attention, achieving lossless, high‑performance inference of the MiMo Flash V2 model on the Kunlun P800 XPU.

Hybrid attentionKunlun P800MiMo Flash V2

0 likes · 8 min read

How vLLM‑Kunlun Plugin Enabled Two‑Day Adaptation of MiMo Flash V2 on Kunlun P800 XPU

Baidu Intelligent Cloud Tech Hub

Jan 5, 2026 · Artificial Intelligence

How Baidu Tianchi Supernodes Supercharge Large‑Model Inference: Architecture, Deployment, and Optimization

This article details Baidu's Tianchi supernode design and software tuning—covering hardware scale‑up, deployment planning, Prefill and Decode stage optimizations, quantization strategies, and communication schemes—to dramatically boost large‑model inference throughput and latency while lowering token‑cost.

AI infrastructurePerformance optimizationlarge-model inference

0 likes · 20 min read

How Baidu Tianchi Supernodes Supercharge Large‑Model Inference: Architecture, Deployment, and Optimization

Baidu Intelligent Cloud Tech Hub

Dec 24, 2025 · Artificial Intelligence

How Context Parallelism Slashes LLM First‑Token Latency by 80% for 128K Tokens

The article explains how the newly merged Context Parallelism (CP) technique in SGLang, combined with DeepSeek V3.2's Sparse Attention architecture, reduces first‑token latency by up to 80% and alleviates memory pressure for ultra‑long 128K‑token sequences, detailing both algorithmic innovations and engineering solutions.

AI infrastructureContext ParallelismDistributed inference

0 likes · 10 min read

How Context Parallelism Slashes LLM First‑Token Latency by 80% for 128K Tokens

Baidu Intelligent Cloud Tech Hub

Dec 17, 2025 · Artificial Intelligence

How AFD Splits Attention and FFN to Boost DeepSeek‑V3 Inference by Up to 19%

The article details the Attention‑FFN Disaggregation (AFD) technique used by Baidu Baige to separate self‑attention and feed‑forward network stages in DeepSeek‑V3 models, describing multi‑stage scheduling, three‑batch overlap, communication optimizations, and performance results that achieve up to 19% throughput improvement under a 100 ms SLO.

3BOAFDAttention-FFN Disaggregation

0 likes · 17 min read

How AFD Splits Attention and FFN to Boost DeepSeek‑V3 Inference by Up to 19%

Baidu Intelligent Cloud Tech Hub

Dec 15, 2025 · Artificial Intelligence

Baidu Baige’s Breakthrough: Orchestrating Giant LLM Inference with Silent Instances

The article details Baidu Baige’s next‑generation distributed inference platform for trillion‑parameter LLMs, explaining how automated orchestration, the FedDeployment abstraction, SplitService unified view, Adaptive HPA predictive scaling, Silent Instances for second‑level activation, and the Staggered Batched Scheduler eliminate scaling limits, reduce TTFT by 30‑40%, boost throughput by up to 20%, and achieve cost‑effective, elastic AI compute.

Distributed inferenceKubernetesLLM

0 likes · 23 min read

Baidu Baige’s Breakthrough: Orchestrating Giant LLM Inference with Silent Instances

Baidu Intelligent Cloud Tech Hub

Dec 10, 2025 · Artificial Intelligence

Accelerate LLM Deployment on Baidu Kunlun XPU with the Open‑Source vLLM‑Kunlun Plugin

The vLLM‑Kunlun Plugin, built on the vLLM hardware‑plugin RFC, lets developers deploy any major large language model on Baidu's Kunlun XPU instantly without modifying vLLM core code, dramatically shortening migration time, providing high‑performance fusion operators, and offering open‑source tools for precision verification and profiling.

InferenceKunlunLLM

0 likes · 8 min read

Accelerate LLM Deployment on Baidu Kunlun XPU with the Open‑Source vLLM‑Kunlun Plugin

Baidu Intelligent Cloud Tech Hub

Dec 4, 2025 · Artificial Intelligence

How Offloading Latent Cache to CPU Boosts DeepSeek‑V3.2‑Exp Decoding Throughput

This report details the analysis of memory bottlenecks in DeepSeek‑V3.2‑Exp, proposes the Expanded Sparse Server (ESS) that offloads latent cache to CPU memory, and demonstrates through high‑fidelity simulation that the approach, combined with cache‑warmup and overlap techniques, can double decoding throughput for long‑context inference.

Cache offloadGPU‑CPU optimizationLLM inference

0 likes · 21 min read

How Offloading Latent Cache to CPU Boosts DeepSeek‑V3.2‑Exp Decoding Throughput

Baidu Intelligent Cloud Tech Hub

Nov 25, 2025 · Artificial Intelligence

Why DeepSeek‑V3.2‑Exp Lost Performance and How a Simple RoPE Fix Restored It

The Baidu Baige team discovered that DeepSeek‑V3.2‑Exp’s long‑context performance lagged behind the official report, traced the issue to a subtle RoPE layout mismatch in the open‑source inference demo, collaborated with DeepSeek to fix it, and verified that the model’s speed and accuracy fully recovered across multiple benchmarks.

AI infrastructureDeepSeekLLM inference

0 likes · 9 min read

Why DeepSeek‑V3.2‑Exp Lost Performance and How a Simple RoPE Fix Restored It