---
title: "How much VRAM should I budget to serve DeepSeek with a long context?"  
description: "How much VRAM should I budget to serve DeepSeek with a long context?"  
author: "Marta Kowalska"  
published: 2026-10-23  
updated: 2026-10-23  
canonical: https://www.mindstick.com/forum/162882/how-much-vram-should-i-budget-to-serve-deepseek-with-a-long-context  
category: "DeepSeek Deployment"  
tags: ["DeepSeek", "Self-hosting", "GPU Memory", "inference"]  
reading_time: 1 minute  

---

# How much VRAM should I budget to serve DeepSeek with a long context?

I’m trying to estimate whether a local DeepSeek deployment will fit on my available GPUs, but model size alone doesn’t seem like enough to plan around. A long context and several simultaneous requests can add a lot of **KV cache memory**, and different serving runtimes seem to report different usable context limits.

What inputs do you use for a realistic VRAM estimate—model variant, context length, concurrency, runtime overhead? I’m looking for a practical way to size a deployment before spending time tuning it.


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Original Source: https://www.mindstick.com/forum/162882/how-much-vram-should-i-budget-to-serve-deepseek-with-a-long-context

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