Architectural Innovations

Mixture of Experts (MoE) Optimization

GLM-5 maintains the MoE architecture with 744B total parameters but activates only 40B during inference through 256 expert modules and 80-layer deep networks. This achieves exponential parameter growth while maintaining computational efficiency:

• Parameter scale increased from 355B (GLM-4.5) to 744B
• Activated parameters rose from 32B to 40B
• Pretraining data expanded from 23T tokens to 28.5T (27T base + 1.5T reinforcement)

Dynamic Sparse Attention (DSA)

The DSA mechanism matches DeepSeek-V3’s performance (943.7B parameters) with just 20B training tokens through three innovations:

• Dynamic gating: Automatically selects attention heads based on input sequence features
• Gradient-aware sparsity: Preserves connections with greatest impact on model outputs
• Hardware-friendly design: Optimizes memory access patterns via grouped convolutions

Experimental results show:

• 62% reduction in memory usage for 16K-length texts
• 3.1× faster inference with <0.7% accuracy loss

Training Framework Breakthroughs

Fully Asynchronous Agent RL Framework

GLM-5 employs decentralized reinforcement learning with independent agents on each training node communicating via message queues. This addresses three bottlenecks in traditional parameter server architectures:

• Communication latency: Reduced by 90% through asynchronous updates
• Scalability: Supports training clusters with >10,000 GPUs
• Fault tolerance: Single-node failures don’t disrupt training

Custom Slime RL Infrastructure

The dedicated Slime RL framework comprises three core components:

# Core components of Slime RL
class SlimeRL:
    def __init__(self):
        self.gradient_compressor = QuantizedCompressor()  # 4-bit gradient quantization
        self.fault_tolerance = CheckpointManager()        # Automatic checkpoint recovery
        self.resource_scheduler = DynamicAllocator()      # Real-time resource allocation

• Gradient compression: 75% reduction in communication volume via 4-bit quantization
• Elastic training: 40% improved resource utilization through dynamic scaling
• Mixed precision: 1.8× acceleration using FP16/FP8 hybrid computation

Multi-Token Prediction Strategy

Unlike traditional single-token prediction, GLM-5 predicts joint probabilities of 5 consecutive tokens, capturing longer semantic dependencies. This delivers:

• 12% higher BLEU scores in code generation
• 9.3% improved accuracy in mathematical reasoning

Performance Evaluation

Benchmark Results

GLM-5 achieved 50 points on AAII v4.0, becoming the first open-source model to break 50 points:

• Language understanding: 52.3
• Logical reasoning: 48.7
• Code generation: 51.1
• Mathematical computation: 46.9

Competitive Rankings

In open-source leaderboards:

• Text generation: ELO 1325 (surpassing Llama-3-70B’s 1298)
• Code generation: ELO 1412 (8.7% above CodeLlama-34B)
• Overall: Tier 1 with Claude Opus 4.5 and Gemini 3 Pro

Resource Efficiency

On 8×A100 80G hardware:
| Model | Throughput (tokens/s) | Memory (GB) | Latency (ms) |
|—————————|———————————-|——————-|———————|
| GLM-5 | 1,250 | 38.2 | 78 |
| Llama-3-70B | 890 | 56.7 | 112 |
| Mixtral-8×22B | 1,020 | 45.1 | 95 |

Future Technical Directions

GLM-5 validates three key trends:

1. Sparse architectures: MoE with dynamic sparsity becomes mainstream
2. Asynchronous training: Decentralized designs boost ultra-large-scale training
3. Multimodal fusion: Next-gen models will integrate text, image, and audio processing

For developers, GLM-5 offers compelling advantages: open-source ecosystem benefits with near-proprietary performance, particularly in long-text processing and code generation. The public release of technical documentation and model weights is expected to accelerate optimization competitions across the AI community.

The model weights, detailed training logs, and configuration specifications are now available for download. Developers can access fine-tuning guidelines through the official documentation to conduct secondary development within compliance frameworks. This performance competition between open-source and proprietary models ultimately benefits the entire AI technology ecosystem.