Stephen Abkin, Alex Flesher, Aldric Yang, Nitheesh Kodarapu, Deepak Panigrahy, David Kebo Houngninou
Quantization can lower the energy of language-model calls and free memory for serving more agents, but its benefit per completed task remains unclear when agents share a GPU. Measurements of fixed requests or isolated agents leave open how the agents' generated work interact with competition for compute and cache. We present a systematic study of precision and admission limits for two agent models on A100 deployments with 40 or 80 GB of memory, using coding and data-analysis tasks with independently verified outcomes. We replay recorded calls across precisions to isolate execution energy and cap the cache to test the value of freed memory. For both models with four admitted agents where requests fit in the cache, 4-bit weights with 16-bit activations execute the same calls with 10-25% less energy than 8-bit weights and activations. Despite this saving, extra work and fewer completions in live runs raise their energy per verified completion by 49%. Where requests compete for cache on the 40 GB deployment, 4-bit weights cost less, and capping their cache raises energy per verified completion by 34% with 16 agents. These results show how generated work and cache demand change the energy benefit of quantization, providing a practical basis for choosing precision and admission limits together.
Stephen Abkin, Aldric Yang, Alex Flesher, Nitheesh Kodarapu, Deepak Panigrahy, Aakash Tyagi, David Kebo Houngninou
Quantization reduces the memory and energy needed to serve large language models (LLMs) and AI agents for coding and data analysis can now run on locally hosted LLMs. In practice, this benefit is commonly benchmarked per output token. Yet an agent calls its model repeatedly, runs tools, and revises its work, and quantization can change both how much the agent generates and whether it succeeds. Existing energy studies either compare quantized models on single requests or measure agents at one fixed precision, overlooking how a change in precision plays out across a complete agent task. To address this gap, this paper presents the first characterization of the task-level energy trade-offs of model quantization in locally hosted agents. We develop 29 agent tasks with automatic pass/fail checks in six families, ranging from single-answer SQL analytics to multi-step repository bug fixing, and measure CPU and GPU energy per output token and per completed task for 4-bit and 8-bit versions of two open-weight models on bare-metal NVIDIA A100 nodes with llama$.$cpp. Our findings reveal that 4-bit precision generally saves energy per token, though whether it saves energy per completed task is task dependent and changes with the model and the execution budget (i.e., limits on turns, tokens, and time). We trace this discrepancy to the extra tokens needed to complete a task in full, which translates into using up to 3.5 times as much energy per completed task. These findings offer actionable guidance for developers and operators choosing model precision for agents, showing how tokens per completed task, set against the per-token saving, reveal whether lower precision completes the work with less energy.