---
title: "What affects DeepSeek throughput when several users share one local GPU?"  
description: "What affects DeepSeek throughput when several users share one local GPU?"  
author: "Davide Romano"  
published: 2026-10-15  
updated: 2026-10-15  
canonical: https://www.mindstick.com/forum/162332/what-affects-deepseek-throughput-when-several-users-share-one-local-gpu  
category: "Local Model Deployment"  
tags: ["DeepSeek", "Local AI", "GPU", "inference"]  
reading_time: 1 minute  

---

# What affects DeepSeek throughput when several users share one local GPU?

I’m considering serving a DeepSeek model locally for a small team, with several people sending requests throughout the day. Most examples I’ve found focus on getting a model to run at all, rather than what happens once requests compete for the same GPU.

For people running **several users on one local GPU**, what has mattered most for usable throughput: model size, quantization, batching, or the serving stack? I’m trying to understand what to measure before choosing a setup, especially when response times matter as much as peak tokens per second.


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Original Source: https://www.mindstick.com/forum/162332/what-affects-deepseek-throughput-when-several-users-share-one-local-gpu

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