GLM-5 Serving Parameter Tuning for OpenClaw: Single-Deployment MaaS Inference Optimization for Long-Context Agent Workloads
📰 ArXiv cs.AI
Optimize GLM-5 serving parameters for long-context agent workloads in OpenClaw using single-deployment MaaS inference optimization to improve throughput and reduce latency
Action Steps
- Configure GLM-5 serving parameters to prioritize throughput and tail latency
- Run experiments to measure the impact of different parameter settings on TTFT and output quality
- Tune the model to achieve optimal performance for long-context workloads
- Apply single-deployment MaaS inference optimization to reduce latency and improve overall system efficiency
- Test the optimized model on a variety of workloads to ensure robustness and reliability
Who Needs to Know This
AI engineers and researchers working on long-context agent workloads can benefit from this article to improve the performance of their models
Key Insight
💡 Single-deployment MaaS inference optimization can significantly improve the performance of GLM-5 models for long-context workloads
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🚀 Optimize GLM-5 serving parameters for long-context agent workloads in OpenClaw and improve throughput and latency 📊
Key Takeaways
Optimize GLM-5 serving parameters for long-context agent workloads in OpenClaw using single-deployment MaaS inference optimization to improve throughput and reduce latency
Full Article
Title: GLM-5 Serving Parameter Tuning for OpenClaw: Single-Deployment MaaS Inference Optimization for Long-Context Agent Workloads
Abstract:
arXiv:2607.02518v1 Announce Type: cross Abstract: OpenClaw requests are dominated by long, tool-augmented prefixes, including system prompts, conversation history, and tool outputs fed back into the context window. For this workload, with about 28k-30k input tokens and 500 output tokens per request, serving quality is governed by throughput, TTFT, and tail latency rather than short-prompt throughput alone. This report studies GLM-5 serving-parameter tuning within a MaaS multi-model inference opt
Abstract:
arXiv:2607.02518v1 Announce Type: cross Abstract: OpenClaw requests are dominated by long, tool-augmented prefixes, including system prompts, conversation history, and tool outputs fed back into the context window. For this workload, with about 28k-30k input tokens and 500 output tokens per request, serving quality is governed by throughput, TTFT, and tail latency rather than short-prompt throughput alone. This report studies GLM-5 serving-parameter tuning within a MaaS multi-model inference opt
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