---
title: "Discussion on \"The Local vLLM Speculative Decoding Blueprint: How I Quadrupled DeepSeek-R1 Inference to 48 Tokens/Sec on Consumer GPUs with...\" | Hashnode"
description: "Discussion on \"The Local vLLM Speculative Decoding Blueprint: How I Quadrupled DeepSeek-R1 Inference to 48 Tokens/Sec on Consumer GPUs with...\". Running state-of-the-art open reasoning models like DeepSeek-R1 (14B, 32B, and distilled variants) on local consumer hardware often feels painfully sluggish. Standard autoregressive generation in fram"
url: "https://hashnode.com/posts/the-local-vllm-speculative-decoding-blueprint-how-i-quadrupled-deepseek-r1-inference-to-48-tokens-sec-on-consumer-gpus-with/6ae8785129fba87ef1b19847"
---

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# [The Local vLLM Speculative Decoding Blueprint: How I Quadrupled DeepSeek-R1 Inference to 48 Tokens/Sec on Consumer GPUs with...](https://lifehacknexus.hashnode.dev/the-local-vllm-speculative-decoding-blueprint-how-i-quadrupled-deepseek-r1-inference-to-48-tokens-sec-on-consumer-gpus-with?utm_source=hashnode&utm_medium=feed)

Running state-of-the-art open reasoning models like DeepSeek-R1 (14B, 32B, and distilled variants) on local consumer hardware often feels painfully sluggish. Standard autoregressive generation in fram

Nov 2 · 18 min read · lifehacknexus.hashnode.dev  
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