GLM-5 Serving Parameter Tuning for OpenClaw: Single-Deployment MaaS Inference Optimization for Long-Context Agent Workloads
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 optimization architecture. The scope is the Single-Node Optimization block of the inference-optimizatio
The proliferation of advanced AI agents and long-context models is driving an immediate need for efficient inference serving, making optimization strategies critical for scaling these workloads.
Optimizing AI inference for long-context workloads is crucial for the economic viability and performance of advanced AI agents, directly impacting their commercial deployment and utility.
This research provides a pathway to more efficient and cost-effective deployment of AI agentic systems, particularly those relying on extensive context windows, by improving serving parameter tuning.
- · AI agent developers
- · Cloud AI service providers
- · Businesses adopting AI agents
- · Hardware manufacturers for inference
- · Inefficient AI serving models
- · Companies with high inference costs
Improved performance and reduced cost for long-context AI agent inference will accelerate their adoption and capabilities.
More powerful and accessible AI agents could disrupt various industries by automating complex white-collar tasks.
The increased demand for optimized inference hardware and software will further concentrate competitive advantages among leading providers in the compute supply chain.
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Read at arXiv cs.AI