When weather forecasting requires AI models to optimize the accuracy of numerical simulations, when biomedical R&D relies on HPC computing power to analyze molecular structures and uses AI to accelerate drug screening, and when autonomous driving simulations demand massive parallel data processing and real-time inference—the deep integration of HPC (High-Performance Computing) and AI has become the core driving force behind breakthroughs in cutting-edge technology.However, during this integration process, the dual demands of “low-latency data transmission” and “high-throughput computing power” have become insurmountable bottlenecks for traditional computing architectures. Bare-metal GPUs, leveraging their native hardware advantages and architectural-level optimizations, are emerging as the key solution to this dilemma, providing ultimate computing power support for HPC and AI integration scenarios.
I. Computing Power Bottlenecks Amid Convergence Demands: Why Are Traditional Architectures "Overwhelmed"?
The convergence of HPC and AI is, at its core, the collaborative operation of “massive parallel computing” and “precise model inference,” imposing dual stringent demands on computing architectures: On one hand, HPC scenarios such as fluid dynamics and quantum chemistry require sustained, stable high-throughput computing power to process terabytes (TB) or even petabytes (PB) of raw data;on the other hand, AI model training and inference require extremely low instruction latency to ensure efficient data transfer between CPUs, GPUs, and memory, thereby preventing slow model convergence or stuttering during inference caused by excessive latency.
Traditional virtualization or cloud server architectures reveal significant shortcomings in this scenario:
First, “performance overhead” at the virtualization layer: resource scheduling by the hypervisor causes a 10%–30% reduction in GPU computing power output, failing to meet HPC’s extreme throughput demands;
Second, "data transmission latency": data in virtualized environments must pass through multiple virtual links, resulting in transmission latency from memory to the GPU typically in the hundreds of microseconds—far from the microsecond-level requirements of real-time AI inference;
Third, "insufficient resource isolation": multi-tenant sharing of hardware resources causes fluctuations in computing power, affecting the stability of HPC results and the inference accuracy of AI models.
II. The Solution for Bare-Metal GPUs: A Dual-Path Approach to Low Latency and High Throughput
The core advantage of bare-metal GPU architecture lies in “native hardware direct access” and “exclusive resource allocation.” Through hardware-level optimization and architectural design, it simultaneously overcomes the dual challenges of low latency and high throughput, perfectly aligning with the convergence needs of HPC and AI. Its core implementation path can be summarized across three dimensions:
1. Hardware Direct Access: Breaking Through Virtualization Barriers to Achieve Microsecond-Level Latency
Bare-metal GPUs utilize "GPU passthrough technology" to directly mount GPU hardware resources onto the CPU bus of a physical server, completely bypassing the intermediate forwarding stages of the virtualization layer.This native mounting method compresses data transmission latency from the CPU cache and memory to GPU memory down to the microsecond range—for example, Yuanjie Computing’s bare-metal GPU nodes achieve memory-to-GPU transmission latency of less than 50 microseconds, representing a more than sixfold improvement over the 300+ microseconds typical of virtualized architectures.
Additionally, by leveraging PCIe 5.0 buses and NVLink high-speed interconnect technology, bare-metal GPU nodes enable point-to-point direct connections between GPUs, with single-link bandwidth reaching up to 600 GB/s. This further reduces data interaction latency during multi-GPU collaborative operations, providing efficient data transmission support for distributed AI training and HPC multi-node parallel computing.
2. Full Resource Exclusivity: Ensuring Stable High-Throughput Computing Power
Bare-metal GPUs provide users with "physical-server-level" resource exclusivity; hardware resources such as CPUs, GPUs, memory, and storage are not shared with other tenants, fundamentally eliminating resource contention and computing power fluctuations common in virtualized environments. Taking the NVIDIA H100 GPU bare-metal node deployed by Yuanjie Computing as an example, a single card delivers up to 716 TFLOPS of FP32 performance,and FP16 performance of up to 3,958 TFLOPS. Paired with 2TB of DDR5 5600MHz memory (offering bandwidth of up to 896GB/s) and four 4TB NVMe SSDs (with total read/write throughput exceeding 10GB/s), this configuration delivers sustained, stable high-throughput computing power, perfectly meeting the massive parallel processing demands of HPC scenarios.
Additionally, the bare-metal GPU supports custom operating systems and driver versions, allowing users to perform in-depth optimization configurations based on the requirements of HPC software (such as ANSYS and GROMACS) and AI frameworks (such as TensorFlow and PyTorch), further unlocking computational potential and enhancing overall throughput efficiency.