ModeSwitch-LLM: A Lightweight Phase-Aware Controller for Cross-Mode LLM Inference on a Single GPU
arXiv:2605.23057v1 Announce Type: new Abstract: ModeSwitch-LLM is a lightweight request-boundary controller for improving single-GPU large language model inference efficiency by routing each request to an appropriate fixed inference mode. Instead of relying on one static serving configuration, the system selects among FP16, quantized modes, speculative decoding, and hybrid modes such as GPTQ plus prefix caching and INT8 plus continuous batching using cheap workload-level features. We evaluate ModeSwitch-LLM on Meta-Llama-3.1-8B-Instruct served on a single NVIDIA A100 GPU. On deployment-style s
The continuous growth in LLM complexity and adoption, coupled with the inherent computational bottlenecks, necessitates immediate solutions for optimizing inference on existing hardware.
Improving single-GPU LLM inference efficiency directly impacts the cost and accessibility of deploying advanced AI models, making them more economical for a wider range of applications and users.
The ability to dynamically select optimal inference modes based on workload features will lead to more efficient compute resource utilization, potentially lowering operational costs for LLM deployments.
- · LLM developers
- · Cloud providers
- · AI application builders
- · NVIDIA
- · Inefficient inference solutions
- · High-latency AI services
Reduced operational costs for deploying LLMs on single GPUs become achievable.
This efficiency gain could accelerate the adoption of complex LLMs in edge computing and smaller-scale deployments.
Increased accessibility might lead to a proliferation of niche AI applications due to lower barrier-to-entry for inferencing.
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Read at arXiv cs.LG