Concurrency slashes vLLM token costs on RTX 3090

An analysis demonstrates that increasing the concurrency of requests processed by a single RTX 3090 GPU can drastically reduce the cost per token when running the Qwen2.5-7B-Instruct model with vLLM. By maintaining 64 requests in flight, the cost per million output tokens dropped to approximately $0.08, a 22-fold decrease compared to processing only one request at a time, which cost $1.81 per million tokens. This highlights that the effective cost is highly dependent on utilization and latency targets, with higher concurrency leading to longer answer times but significantly lower per-token expenses. AI

IMPACT Demonstrates how optimizing GPU utilization through concurrency can significantly reduce inference costs for LLMs.

RANK_REASON Analysis of model inference costs and performance optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• qaisermehdi3-coder
• qvunex
• Qwen2.5-7B-Instruct
• RTX 3090
• vLLM

• infra
• product

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