Why ARM Is Overtaking x86 in Telecom Servers: Instruction Sets, Compilers, and OS Guidance

Background: Homogeneous vs Heterogeneous Computing

Servers can be built with homogeneous (single‑architecture) CPUs or heterogeneous systems that combine CPUs with specialized accelerators such as GPUs, NPUs, and FPGAs. Homogeneous servers rely on the CPU for all workloads, while heterogeneous designs are driven by AI, big‑data, VR/AR and other parallel workloads.

CPU Instruction‑Set Landscape

The market is split between Complex Instruction Set Computing (CISC) – dominated by the x86 ISA (Intel, AMD) – and Reduced Instruction Set Computing (RISC) – where ARM is the leading ISA. In 2019 x86 accounted for >99.5 % of shipments (Intel ≈ 95.5 %, AMD ≈ 4.5 %). ARM, originally for mobile and embedded devices, now targets servers, high‑performance computing and desktops. Other RISC families include MIPS (e.g., Loongson) and Alpha‑derived architectures (e.g., Sunway).

Domestic Chinese CPU Developments

Chinese vendors typically obtain ISA licenses or cooperate with foreign partners:

x86 : HaiGuang (via AMD) produces a 14 nm, 32‑core, 3.2 GHz CPU based on AMD’s Naples core; its second generation is already in mass production. Zhaoxin’s KX‑6000 series uses a 16 nm process, 8 cores at 3 GHz, targeting desktops and servers.

ARM : Huawei Kunpeng 920 (64 cores, 2.6 GHz, 7 nm) and Feiteng TengYun S2500 (64 cores, 2.1 GHz, 16 nm) are full‑stack ARM designs deployed in telecom data centers.

MIPS/Alpha : Loongson (MIPS) and Sunway (Alpha) continue to ship niche products for specific workloads.

Heterogeneous Accelerators

Accelerator categories and leading vendors:

GPU : Nvidia (≈70‑90 % AI/graphics market share), AMD, Intel. GPUs excel at dense parallelism for AI training, inference, video rendering and cloud gaming.

NPU : Huawei, Cambricon. Optimized for neural‑network inference and training in vision, speech and recommendation systems.

FPGA : Xilinx, Intel, and Chinese firms such as Unisoc, Guangdong Gaoyun, Fudan Microelectronics. Provide re‑configurable logic for high‑parallelism tasks in data centers, communications, aerospace and defense.

Telecom‑Industry Recommendations

1. ARM Instruction‑Set Version

Adopt ARMv8.0 as the baseline ISA for telecom data‑center CPUs and target ARMv8.2 as the recommended standard. Review annually to incorporate newer ARM releases that address telecom‑specific features such as SVE and security extensions.

2. Compiler

Use a stable GCC release for network‑cloud workloads to guarantee reliability. GCC 9 is recommended for current deployments because it adds stronger auto‑vectorization, loop unrolling, full C++17 support and ARM‑specific extensions (Cortex‑A76, Neoverse N1). For future cycles, consider GCC 10 (released >1 year ago) which fully supports Scalable Vector Extension (SVE) and provides the highest performance on ARMv8.2‑compatible cores.

3. Operating System and Kernel

Standardize on the following long‑term‑support Linux distributions for telecom servers:

Ubuntu Kylin 18.04 LTS or newer

SUSE 12.5 or newer

openEuler LTS 20.03 or newer

All distributions should run Linux kernel 4.18 or later; newer kernels (e.g., 5.4 in Ubuntu 20.04) provide expanded device drivers, exFAT support, improved Nouveau graphics handling, and additional network‑interface drivers.

4. Stack Integration Considerations

When deploying heterogeneous workloads, ensure that the OS kernel includes drivers for the chosen accelerators (GPU, NPU, FPGA) and that the compiler toolchain can generate code for the accelerator SDKs. Verify that the selected Linux distribution supports container runtimes (Docker, Kata) and virtualization technologies (KVM, QEMU) required by telecom service‑oriented architectures.