How Baidu’s AIAK‑LLM Supercharges Large‑Model Training and Inference

1. Background and Challenges

Large language models are growing exponentially—often an order of magnitude every 1‑2 years—driving proportional increases in data volume and compute cost. Training such models can require thousands of GPUs (e.g., GPT‑4 on 10‑25k cards, GPT‑5 projected on >50k H100s) and still achieve only 30‑50% hardware utilization, making massive, stable, and efficient AI clusters essential.

2. MFU Metric and Industry Techniques

MFU (Model FLOPS Utilization) measures the ratio of actual FLOPS achieved to the chip’s theoretical peak. For example, an A800 delivering 100 TFLOPS on a 315 TFLOPS‑rated chip yields an MFU of ~32%.

Industry efforts to raise MFU focus on four parallelism dimensions (BSHL): Batch (data parallel), Sequence (S‑parallel), Hidden‑size (tensor parallel), and Layer (pipeline parallel). Advanced strategies include 2D/3D tensor slicing, 1F1B and interleaved pipeline schemes, and sophisticated memory‑saving methods such as Zero‑1/2/3 and recompute techniques.

3. Baidu AIAK‑LLM Suite

Baidu’s AIAK‑LLM platform (now at version 3.0) provides three layers:

Resource layer : large‑scale, stable, cost‑effective compute and storage for heterogeneous workloads.

Component layer : scheduling, fault‑tolerance, and other services that enable stable large‑scale training and inference.

Acceleration layer : optimized kernels, I/O paths, and accelerator abstractions that maximize hardware efficiency.

4. Training Optimizations

Key bottlenecks identified in trace analysis include excessive Tensor‑Parallel (TP) communication, heavy recompute overhead, element‑wise operator costs, and sub‑optimal parallel‑parameter selection.

Solutions applied:

Overlap TP communication with gradient computation by splitting GEMM into smaller chunks and pipelining compute‑communication.

Hybrid recompute strategy that mixes full‑block and selective recompute to balance memory savings and extra compute.

Zero‑offload: move optimizer/parameter/gradient data to CPU memory and pull on‑demand, reducing GPU memory pressure and enabling larger batch sizes or finer‑grained parallelism.

Automated parallel‑strategy search: model‑and‑cluster‑aware enumeration combined with offline performance models to predict compute, memory, and communication costs, yielding near‑expert configurations in minutes.

Multi‑chip support: abstract accelerators for GPUs, Kunlun, Ascend, etc., and unified parallel strategies that achieve high MFU across heterogeneous hardware.

5. Inference Optimizations

Two major inefficiencies were identified: large token‑gap latency and low GEMM MFU in the decoder due to small‑m dimensions.

Optimizations include:

Moving sampling and post‑processing steps onto the GPU and parallelizing them with multi‑process pipelines.

Redesigning the scheduler in C++ to parallelize stop‑check and sequence‑level operations.

Improving GEMM efficiency by increasing effective matrix dimensions and using a small‑model‑first token generation strategy that lets a lightweight model pre‑fill multiple positions before the large model refines them, boosting decoder MFU by up to 60% in low‑latency scenarios.

Eliminating padding waste via 1‑D sequence concatenation (instead of 2‑D padding) and providing extensible hooks for tokenization, preprocessing, and postprocessing.

These changes raise inference throughput by >60% for most models.

6. Product Integration

AIAK‑LLM is integrated with the Hugging Face ecosystem, offering one‑click checkpoint conversion, precision‑alignment tools, and performance visualizers. Together with over 20 native model adapters, customers can achieve immediate, measurable speed‑ups compared with vanilla Hugging Face pipelines.

Overall, Baidu’s AIAK‑LLM suite addresses the three core AI‑infra challenges—scale, stability, and cost—by delivering ultra‑large, fault‑tolerant clusters, aggressive MFU improvements, and seamless integration for downstream applications.