GLM-5 Achieves Open-Source Leadership Without NVIDIA GPUs

GLM-5, NVIDIA 없이 오픈소스 1위 달성 — Phi-4, Qwen3.5까지, 오픈소스 LLM 경쟁이 뜨겁다

Zhipu AI released GLM-5, a 744B parameter model that achieved a 77.8% score on SWE-bench Verified, outperforming all other open-source models. Remarkably, the model was trained entirely on Huawei Ascend chips, bypassing the need for NVIDIA hardware and proving the viability of alternative silicon ecosystems.

Why This Matters

The dominance of NVIDIA’s hardware and CUDA software has created a significant barrier for global AI development, but GLM-5 demonstrates that frontier-level performance is achievable on alternative platforms like Huawei Ascend. This shift suggests that technical optimizations in MoE (Mixture-of-Experts) architectures and software-hardware co-design can overcome international supply chain constraints and high training costs.

Key Insights

• GLM-5 (2026) utilizes a 744B parameter MoE structure with 40B active parameters to reach the top open-source rank on SWE-bench Verified.
• Microsoft’s Phi-4-Reasoning-Vision-15B (2026) introduces adaptive chain-of-thought, which dynamically activates reasoning only for complex logical tasks.
• Alibaba’s Qwen3.5-397B-A17B (2026) achieved an 8.6x to 19x improvement in decoding throughput compared to previous generation models.
• The training of Phi-4 required only 4 days using 240 NVIDIA B200 GPUs, highlighting massive gains in multimodal training efficiency.
• GLM-5 is released under the MIT License, providing significantly more commercial freedom than the custom licenses used by Meta’s Llama series.
• Infrastructure tools like vLLM (72K+ stars) and Ollama (164K+ stars) have become the production standards for serving these high-parameter models locally.

Practical Applications

• Local Multimodal Execution: Running Phi-4-Reasoning-Vision-15B on consumer hardware like M4 Max MacBook for private image analysis.
• Autonomous Software Engineering: Integrating GLM-5 with platforms like OpenHands (68K+ stars) to resolve complex GitHub issues automatically.
• High-Efficiency Inference: Leveraging Qwen3.5 with vLLM for high-throughput agentic workflows where low latency is critical for user experience.
• Pitfall: Forcing step-by-step reasoning on simple queries; use Phi-4’s adaptive CoT to prevent unnecessary token consumption and latency.

References:

• https://dev.to/ji_ai/glm-5-nvidia-eobsi-opeunsoseu-1wi-dalseong-phi-4-qwen35ggaji-opeunsoseu-llm-gyeongjaengi-ddeugeobda-4lfd