The MLPerf 2024 benchmark shows that the cloud server cluster with the latest architecture has a computing power density of more than 3.1 PFlops/m³ and a reasoning energy efficiency ratio of 2.8 TOPS/W. IDC predicts that by 2026, AI computing power demand will account for 43% of global server shipments, driving structural changes in the underlying technology.
Heterogeneous computing architecture breakthrough
The NVIDIA Grace Hopper Superchip integrates CPU and GPU memory space through 900GB/s NVLink-C2C interconnection, reducing latency in Llama 2-70B inference tasks by 57%. The AMD Instinct MI300X is packaged with 3D Chiplet, integrates 24 Zen4 cores and CDNA3 computing units, reaches 389 TFLOPS of FP8 tensor peak computing power, and 192GB of HBM3 video memory to triple the size of 100 billion parameter model training batches.
Intel Falcon Shores XPU innovative integration of x86 CPU and GPU architecture, through EMIB technology to achieve 1.6TB/s chip bandwidth, in molecular dynamics simulation performance than the traditional architecture 4.3 times. Domestic computing solutions have also made breakthroughs, Huawei Centeng 910B adopts Da Vinci architecture, supports CANN 7.0 heterogeneous computing framework, and achieves 92% linear expansion efficiency in ERNIE 3.0 Titan training.
Intelligent resource arrangement system
Kubernetes 1.30 introduces topologically aware scheduling plug-ins that dynamically adjust Pod distribution based on NVIDIA DCGM monitoring data, increasing GPU utilization from 61% to 88%. Microsoft Azure SynapseML platform integrates Fluid framework, implements memory-level data caching through Alluxio, and reduces the ResNet-152 training IO wait time to 1.7 seconds /epoch.
Reinforcement learning scheduler has become a new trend, Alibaba Cloud Ack One adopts DQN algorithm to deal with multidimensional constraints, and the median task queuing time in a 5000-node cluster is reduced to 47 seconds. Dynamic Voltage Frequency regulation (DVFS) technology to achieve fine-grained power control, Google TPU v4 through the TensorFlow Runtime power sensing scheduling, saving 29% of the same computing power.
Hyperconverged network architecture
The NVIDIA Quantum-3 InfiniBand switch is equipped with a 7nm process chip, and the single-port rate is increased to 800Gb/s. Combined with the adaptive routing algorithm, the Allreduce operation latency of the 4096-node cluster is stable at 0.9μs±5%. Meta's Dragonfly++ topology controls the global diameter within 3 hops, and with the RoCEv2 congestion control protocol, achieves 98% bandwidth utilization at 4000 nodes.
The silicon optical integration technology has entered the mass production stage, and the Intel 1.6T CPO optical engine uses a hybrid bonding process to narrow the laser and electrical chip spacing to 10μm, and the module power consumption is reduced to 4.5pJ/bit. Coherent's 800G ZR+ optical module supports 120km single-mode transmission with a bit error rate lower than 1E-15, providing a physical basis for cross-region pooling.
Energy efficiency management revolution
The submersible liquid cooling system has achieved a breakthrough of PUE 1.05. Alibaba Renhe Data Center adopts fluorinated liquid two-phase cooling, the power density of a single cabinet reaches 80kW, and the chip junction temperature fluctuation is controlled at ±2℃. 3M Novec 7100 dielectric fluid reduces H100 entire card power consumption by 18% in GPU direct cooling applications.
When the intelligent power distribution system is upgraded to the third-generation, Huawei FusionPower uses the LSTM algorithm to predict load fluctuations and dynamically adjust the phase balance, so that the UPS efficiency reaches 99%. The application of regenerative braking technology in the backup power supply increases the fuel efficiency of diesel generators by 23%.
Enterprise application practice
An autonomous driving company deployed a resilience training framework in a 1,500-card cluster:
Using dynamic elastic batch processing technology, task interruption recovery time is reduced from 17 minutes to 42 seconds
Combined with the automatic capacity expansion strategy, the resource idle rate was reduced from 35% to 6%
Using real-time preprocessing of PB-level point cloud data, the training iteration speed is increased by 3.8 times
Technical selection suggestion
Kilocalorie the following clusters: Priority is given to the RoCEv2 network +FP8 precision training scheme
Large-scale training: SHARP aggregated computing InfiniBand switches need to be configured
Edge inference scenario: Grace Hopper unified memory architecture is recommended
Click on the Jtti official website to get the customized architecture design solution, and the professional technical team will provide the TCO optimization solution according to your business scenario, which is expected to reduce the computing cost by 28%.
