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Baidu Geek Talk
Baidu Geek Talk
Dec 24, 2025 · Artificial Intelligence

Context Parallelism Slashes TTFT by 80% for 128K-Token LLMs

The article explains how Baidu’s Baige team integrated a Context Parallelism strategy into DeepSeek V3.2, detailing the DSA architecture, the limitations of traditional tensor and sequence parallelism, and how CP distributes computation and memory across GPUs to achieve up to an 80 % reduction in token‑to‑first‑token latency for ultra‑long 128K‑token contexts.

Context ParallelismDeepSeekLLM
0 likes · 9 min read
Context Parallelism Slashes TTFT by 80% for 128K-Token LLMs
Baidu Intelligent Cloud Tech Hub
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
Baidu Intelligent Cloud Tech Hub
Nov 4, 2025 · Artificial Intelligence

How Baidu’s Baige Accelerates Multimodal Video Training with Context Parallelism

Baidu Baige’s enhanced veRL framework dramatically boosts video frame rates and resolution limits, cuts training time, reduces memory usage, and improves model accuracy by leveraging context parallelism and optimized attention on Ampere GPUs for multimodal mixed‑training scenarios.

AI accelerationContext ParallelismMultimodal Training
0 likes · 6 min read
How Baidu’s Baige Accelerates Multimodal Video Training with Context Parallelism
AI Algorithm Path
AI Algorithm Path
May 11, 2025 · Artificial Intelligence

How to Parallelize Ultra‑Large Model Training with PyTorch

The article explains the core concepts and trade‑offs of five parallelism techniques—data, tensor, context, pipeline, and expert parallelism—plus the ZeRO optimizer, showing when each method is appropriate for training ultra‑large PyTorch models and providing concrete code snippets and performance considerations.

Context ParallelismData ParallelismExpert Parallelism
0 likes · 21 min read
How to Parallelize Ultra‑Large Model Training with PyTorch