glm-5#

  • Context Length: 202752

  • Model Name: glm-5

  • Languages: en, zh

  • Abilities: chat, vision, tools, reasoning

  • Description: We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity. Reinforcement learning aims to bridge the gap between competence and excellence in pre-trained models. However, deploying it at scale for LLMs is a challenge due to the RL training inefficiency. To this end, we developed slime, a novel asynchronous RL infrastructure that substantially improves training throughput and efficiency, enabling more fine-grained post-training iterations. With advances in both pre-training and post-training, GLM-5 delivers significant improvement compared to GLM-4.7 across a wide range of academic benchmarks and achieves best-in-class performance among all open-source models in the world on reasoning, coding, and agentic tasks, closing the gap with frontier models.

Specifications#

Model Spec 1 (pytorch, 744 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 744

  • Quantizations: none

  • Engines: vLLM, Transformers

  • Model ID: zai-org/GLM-5

  • Model Hubs: Hugging Face, ModelScope

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name glm-5 --size-in-billions 744 --model-format pytorch --quantization ${quantization}

Model Spec 2 (fp8, 744 Billion)#

  • Model Format: fp8

  • Model Size (in billions): 744

  • Quantizations: FP8

  • Engines: vLLM

  • Model ID: zai-org/GLM-5-FP8

  • Model Hubs: Hugging Face, ModelScope

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name glm-5 --size-in-billions 744 --model-format fp8 --quantization ${quantization}

Model Spec 3 (ggufv2, 744 Billion)#

  • Model Format: ggufv2

  • Model Size (in billions): 744

  • Quantizations: UD-TQ1_0

  • Engines: vLLM, llama.cpp

  • Model ID: unsloth/GLM-5-GGUF

  • Model Hubs: Hugging Face, ModelScope

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name glm-5 --size-in-billions 744 --model-format ggufv2 --quantization ${quantization}

Model Spec 4 (mlx, 744 Billion)#

  • Model Format: mlx

  • Model Size (in billions): 744

  • Quantizations: 4bit, 8bit-MXFP8

  • Engines: MLX

  • Model ID: mlx-community/GLM-5-{quantization}

  • Model Hubs: Hugging Face, ModelScope

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name glm-5 --size-in-billions 744 --model-format mlx --quantization ${quantization}