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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.22402 |
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| _version_ | 1866912791065001984 |
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| author | Vangala, Bhanu Prakash Malik, Tanu |
| author_facet | Vangala, Bhanu Prakash Malik, Tanu |
| contents | Self-hosting large language models (LLMs) is increasingly appealing for organizations seeking privacy, cost control, and customization. Yet deploying and maintaining in-house models poses challenges in GPU utilization, workload routing, and reliability. We introduce Pick and Spin, a practical framework that makes self-hosted LLM orchestration scalable and economical. Built on Kubernetes, it integrates a unified Helm-based deployment system, adaptive scale-to-zero automation, and a hybrid routing module that balances cost, latency, and accuracy using both keyword heuristics and a lightweight DistilBERT classifier. We evaluate four models, Llama-3 (90B), Gemma-3 (27B), Qwen-3 (235B), and DeepSeek-R1 (685B) across eight public benchmark datasets, with five inference strategies, and two routing variants encompassing 31,019 prompts and 163,720 inference runs. Pick and Spin achieves up to 21.6% higher success rates, 30% lower latency, and 33% lower GPU cost per query compared with static deployments of the same models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22402 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Multi-Model Orchestration for Self-Hosted Large Language Models Vangala, Bhanu Prakash Malik, Tanu Distributed, Parallel, and Cluster Computing Artificial Intelligence Self-hosting large language models (LLMs) is increasingly appealing for organizations seeking privacy, cost control, and customization. Yet deploying and maintaining in-house models poses challenges in GPU utilization, workload routing, and reliability. We introduce Pick and Spin, a practical framework that makes self-hosted LLM orchestration scalable and economical. Built on Kubernetes, it integrates a unified Helm-based deployment system, adaptive scale-to-zero automation, and a hybrid routing module that balances cost, latency, and accuracy using both keyword heuristics and a lightweight DistilBERT classifier. We evaluate four models, Llama-3 (90B), Gemma-3 (27B), Qwen-3 (235B), and DeepSeek-R1 (685B) across eight public benchmark datasets, with five inference strategies, and two routing variants encompassing 31,019 prompts and 163,720 inference runs. Pick and Spin achieves up to 21.6% higher success rates, 30% lower latency, and 33% lower GPU cost per query compared with static deployments of the same models. |
| title | Efficient Multi-Model Orchestration for Self-Hosted Large Language Models |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2512.22402 |