Fine-Tune GLM-5 with Ertas

Overview

GLM-5, released by Z.ai (formerly Zhipu) on February 11, 2026, is the foundational base of the GLM-5 series — a 745-billion parameter dense model trained on Huawei Ascend chips. GLM-5 was succeeded two months later by GLM-5.1 (April 8, 2026), which used the same base architecture but added refined post-training that delivered a 28% coding improvement and 8-hour autonomous run capability. For new deployments, GLM-5.1 is the recommended choice; GLM-5 remains relevant as the foundational release and for teams running stable production deployments that adopted it before the 5.1 update.

A notable detail of GLM-5's training is that it was trained on Huawei Ascend chips rather than NVIDIA hardware — making it one of the first frontier-scale open-weight models trained on alternative AI accelerator infrastructure. This has implications for the geopolitical and supply-chain narrative around AI training, though for most deployment teams the architectural and quality characteristics matter more than the training hardware.

Z.ai went public on the Hong Kong Stock Exchange on January 8, 2026, signaling significant institutional interest in the company's AI infrastructure positioning. GLM-5 builds on the GLM-4.5 (July 2025) architecture and post-training methodology, scaled up substantially in parameter count and training data. The model's positioning emphasizes Claude Code-style agentic coding capability — making it a credible self-hosted alternative for teams evaluating GLM-4.6 or similar models in this niche.

Weights are available on Hugging Face under `zai-org/GLM-5`. The license terms are commercial-permissive but worth reviewing for specific deployment scenarios.

Key Features

BenchLM aggregate score in the high 70s places GLM-5 in the top tier of open-weight models — not at the absolute leaderboard top (DeepSeek V4 at 87, Kimi K2.6 at 86) but solidly competitive with the second-tier flagships at release. The GLM-5.1 update lifted this further (BenchLM 83) through post-training refinements alone, demonstrating substantial unrealized capability in the GLM-5 base. The model's strengths are particularly pronounced on coding and reasoning benchmarks, where GLM-5 substantially outperforms its predecessor GLM-4.5.

Training on Huawei Ascend chips is a notable infrastructure detail. While the model architecture and behavior aren't fundamentally different from NVIDIA-trained equivalents, this represents one of the first frontier-scale open-weight models from a non-NVIDIA training pipeline. For teams interested in supply-chain diversity or in regions where NVIDIA hardware access is constrained, GLM-5's training provenance may be relevant.

The Claude Code-alternative positioning — emphasizing agentic coding capability — makes GLM-5 well-suited for self-hosted coding agent deployments. While MiMo V2.5 Pro and Kimi K2.6 lead the open-weight coding benchmarks, GLM-5 is a credible alternative particularly for teams in regions where Z.ai's regional support and ecosystem are strong advantages.

Z.ai's IPO on the Hong Kong Stock Exchange provides ongoing institutional backing that should support continued model investment and ecosystem development. For teams evaluating long-term commitments to specific Chinese-lab open-weight models, this provides additional confidence beyond the model release itself.

Fine-Tuning with Ertas

GLM-5 at 745B parameters is at the upper end of practical fine-tuning. Ertas Studio supports QLoRA fine-tuning on multi-GPU server configurations (8x A100 80GB or larger), with approximately 450-550GB of total VRAM required at typical sequence lengths.

For most teams without 8-GPU server access, the recommended pattern is teacher-student distillation: use GLM-5 as a teacher to generate synthetic training data, then fine-tune a smaller base model (Qwen 32B, Llama 70B, or GLM-4.5 itself) on that data. GLM-4.5 at 355B/32B active is a more accessible distillation target than GLM-5 directly.

After training, Ertas Studio exports GLM-5 fine-tunes to GGUF format. The Q4_K_M quantization is approximately 380GB — server-grade deployment. For most teams interested in GLM capability without the multi-GPU footprint, fine-tuning GLM-4.5 directly or distilling onto smaller bases is the more practical path.

Use Cases

GLM-5 is best-suited to teams running multi-GPU server infrastructure who want a high-quality open-weight alternative to DeepSeek V4 or Kimi K2.6. Particularly compelling for organizations with strong ties to the Z.ai ecosystem or regional preferences for Chinese-lab models trained on alternative infrastructure.

Agentic coding deployments are a natural fit given the Claude Code-alternative positioning. Teams self-hosting coding agents who want to evaluate multiple Chinese-lab options often include GLM-5 alongside MiMo V2.5 Pro and Kimi K2.6 in their assessment.

For teams in regions where NVIDIA hardware is constrained or where supply-chain diversity is a strategic concern, GLM-5's training on Huawei Ascend is a meaningful detail — both for the model itself and as a signal that frontier-scale open-weight training can happen on alternative accelerators.

Hardware Requirements

GLM-5 at Q4_K_M quantization requires approximately 380GB of memory, fitting on an 8x A100 80GB or 8x H100 80GB server, or a CPU inference host with 512GB+ RAM. The dense architecture means active and total parameter counts are the same — generation throughput corresponds to a 745B dense model, which is meaningfully slower per token than equivalent-quality MoE models like Kimi K2.6 (32B active) or DeepSeek V4 (49B active).

For smaller deployments, Q3_K_M quantization (approximately 290GB) trades modest quality for reduced memory, fitting on a 4x H100 80GB server with margin.

For fine-tuning in Ertas Studio: GLM-5 QLoRA needs approximately 450-550GB total VRAM (multi-GPU server). For teams without that scale, GLM-4.5 fine-tuning (with its 32B active parameter MoE architecture) is substantially more accessible — fitting on a 80GB GPU at QLoRA training-time memory requirements.