I. Paradigm Innovation in China’s AI Computing Ecosystem

As AI models scale beyond trillion parameters and training clusters approach 10,000-card configurations, traditional closed hardware ecosystems reveal significant limitations. A Chinese AI chip manufacturer has established a full-stack technology system covering operator development, communication scheduling, and cluster deployment by opening up底层 programming interfaces (original Chinese term maintained for technical precision) and instruction set architectures. Key breakthroughs manifest in three dimensions:

1. Hardware Abstraction Layer Opening
The stable foundation programming interface (similar to Ascend C) enables direct optimization of memory access paths and computational pipelines. Benchmark tests show manual-tuned GEMM operators achieve 3-5x performance improvement over auto-generated versions.

2. Hybrid Programming Paradigm Support
The ecosystem supports both low-level optimization through instruction sets (e.g., PTO ISA) for veteran engineers and high-level domain-specific languages (e.g., TileLang) to lower development barriers. Open-source community tests demonstrate TileLang reduces attention operator code volume by 70% while maintaining <15% performance loss.

3. Unified Heterogeneous Computing Framework
A unified intermediate representation (IR) enables CPU/NPU collaborative computing. In long-sequence inference scenarios, the KV Cache offloading scheme reduces GPU memory usage by 40% while maintaining >95% computational efficiency.

II. Technical Deconstruction of High-Performance Operator Libraries

The open-source core components comprise four operator libraries and a distributed communication framework forming a complete computational acceleration matrix:

1. Compute-Intensive Operator Optimization

• DeepGEMM: Achieves 92% SM unit utilization in FP16 precision through tiled matrix multiplication and register reuse. Dynamic tiling strategy automatically adapts optimal computation paths for different matrix dimensions.
• FlashMLA: Develops dedicated instruction pipelines for Transformer’s masked self-attention mechanism, reducing masked computation latency from 12ms to 2.3ms at 128K sequence length.

2. Memory Access Optimization Technologies

• TileKernel: Introduces 3D memory tiling strategy converting global memory access to shared memory access, improving memory bandwidth utilization from 68% to 91% in ResNet-152 inference.
• DeepSelect: Enables hardware acceleration for dynamic sparse computing, achieving 85% efficiency of dense computation for unstructured sparse matrices through bitmap compression and prefetching.

III. Ultra-Large-Scale Cluster Communication Architecture

Addressing communication bottlenecks in thousand-card training clusters, the Chinese solution achieves breakthroughs through hardware-software co-design:

1. Topology-Aware Communication Libraries

• ASC-COMM: Provides custom communication primitives supporting topology-aware scheduling for AllReduce/AllGather operations, maintaining gradient aggregation latency <120μs in 128-card single-layer switched networks.
• DeepEP: Covers communication operators for EP/CP/PP/FSDP parallel modes, achieving 65% communication-computation overlap through pipeline overlapping technology. Benchmarks show 375GB/s send and 347GB/s receive bandwidth at 3.2Tbps network speed.

2. Super-Node Network Design

The two-layer switching architecture enables 256K-card cluster expansion:

• Scale-up Layer: 128 cards form super-nodes with full-mesh topology eliminating communication hotspots
• Scale-out Layer: Fat-tree topology between super-nodes supports 200ns port forwarding latency
• Dynamic Traffic Scheduling: RDMA-based congestion control algorithm maintains >92% effective bandwidth utilization

IV. End-to-End Deployment Practice Guide

1. Inference Performance Optimization Path

Taking DeepSeek-V4.1-Flash model as example under EP32 deployment strategy:

# Key performance tuning parameters configuration example
config = {
    "batch_size": 64,
    "sequence_length": 128*1024,
    "precision": "fp16_bf16_mix",
    "kernel_launch_mode": "async_grouped",
    "communication_overlap": 0.65
}

• Offline Inference Mode: Achieves 2,469 tokens/s per card output throughput under 5ms TPOT constraint
• Long-Sequence Processing: KV Cache pooling technology increases memory usage by only 18% for 200K sequence inference
• Speculative Sampling Optimization: Maintains 0.85 acceptance rate while improving generation speed by 2.3x

2. Training Efficiency Improvement Scheme

In thousand-card cluster training scenarios, hybrid parallel strategies are employed:

• Data Parallelism: Implements gradient sharding and communication compression using FSDP
• Tensor Parallelism: Reduces communication volume by 40% through matrix operation dimension splitting
• Pipeline Parallelism: Achieves 82% device utilization with 1F1B scheduling eliminating bubbles

Benchmark data shows 70B parameter model training achieves 52% MFU (Model FLOPs Utilization), delivering sustained performance of 3.8×10¹⁵ FLOPS/s on 2,048-card clusters.

V. Ecosystem Co-Construction and Technology Evolution Directions

The open-source community has formed three primary technology evolution paths:

1. Compiler Optimization Toolchain: Developing MLIR-based graph-level optimizers for automatic generation of efficient operator implementations
2. Heterogeneous Scheduling Framework: Building unified resource scheduling layers enabling dynamic load balancing across CPU/NPU/DSA
3. Automated Tuning Platform: Combining reinforcement learning to automatically search optimal parallel strategies and hyperparameter combinations

A joint laboratory established between the Chinese chip manufacturer and leading algorithm teams is developing next-gen optical interconnect technology (OCS) and 3D packaging architectures, targeting minute-level fault recovery capabilities for million-card clusters by 2025. This “hardware openness + algorithm co-construction” model is reshaping technical standards and industrial landscapes of AI infrastructure.

Conclusion: The breakthrough in China’s AI computing ecosystem represents not just performance metric improvements but the establishment of a sustainable technology system. Through open-sourcing foundational components, opening programming interfaces, and co-constructing performance benchmarks, an open collaborative innovation ecosystem is taking shape. For AI developers, this means richer tooling options, lower development barriers, and historical opportunities to participate in defining next-gen AI infrastructure.