Unlocking 8‑Hour Autonomous Coding: GLM‑5.1’s Leap with Kunlun XPU

GLM‑5.1 Model Overview

GLM‑5.1, the latest open‑source large language model from Zhipu AI, achieves the highest global scores on the SWE‑bench Pro benchmark, surpassing GPT‑5.4 and Claude Opus 4.6, especially in code generation. It also demonstrates a long‑horizon capability, maintaining autonomous execution on a single task for up to eight hours.

Rapid Adaptation to Kunlun XPU

Baidu Baige adapted GLM‑5.1 to the Kunlun XPU platform using a Prefill‑Decode separation architecture combined with Context Parallelism (CP). CP reduces compute load and memory pressure for sequences longer than 128 K tokens, enabling high‑concurrency AI‑Agent and coding workloads.

The vLLM‑Kunlun Plugin decouples the community vLLM engine from the Kunlun XPU backend, allowing the XPU to be used like a standard GPU. Models that do not introduce new operators can be adapted on Day 0; models with new operators require only the development of corresponding kernels.

Full‑Stack Performance Optimizations

Custom Kunlun operators replace framework bottlenecks.

CUDA‑Graph integration eliminates CPU scheduling overhead, dramatically lowering kernel‑launch latency.

For quantization, an end‑to‑end pipeline spans model, framework, and hardware layers. Using Kunlun’s self‑developed quantization toolchain, INT4 mixed‑precision quantization of a 754 B‑parameter model on a single Kunlun P800 supports 64 K token sequences with a 20 % inference speedup.

Long‑Context and KV‑Cache Optimizations

Advanced KV‑Cache scheduling and acceleration engines achieve 80‑90 % cache‑hit rates, reducing the time‑to‑first‑token (TTFT) for 64 K sequences by 6.2×, which is critical for AI‑Agent and complex coding scenarios that require extremely long texts and high concurrency.

Scalable Cluster Deployment

Baige further optimized cluster inference with a Prefill‑Decode (PD) separation architecture and released standardized deployment solutions for both 8‑card servers and super‑node hardware.

In a traditional 8‑card server configuration, six Kunlun P800 machines can handle GLM‑5.0 inference for 200 K token sequences.

In a super‑node configuration, Prefill performance improves by >16 % and Decode by >17 % compared with a single‑node 8‑card setup.

Elastic scaling reduces instance startup time from minutes to seconds, supporting rapid elasticity for fluctuating traffic.

Kunlun Infrastructure Scale‑Out

By early 2025, Baidu Cloud deployed a 10 000‑card Kunlun P800 AI cluster, later expanded to 32 000 cards. The Tianchi super‑node solution uses a 32‑card full‑mesh interconnect with sub‑microsecond latency, delivering ultra‑low per‑token cost while remaining compatible with existing data‑center environments.

These combined capabilities—day‑zero model adaptation, full‑stack performance tuning, quantization, long‑context support, and massive scale‑out—enable efficient production deployment of cutting‑edge LLMs such as GLM‑5.1 on Kunlun XPU.