---
title: "Accelerate Mixtral 8x7B with Speculative Decoding and Quantization on Amazon SageMaker"
url: https://daily.dev/posts/accelerate-mixtral-8x7b-with-speculative-decoding-and-quantization-on-amazon-sagemaker-bl0lvog0i
source_url: https://www.philschmid.de/sagemaker-awq-medusa
type: article
source: "Philipp Schmid"
published: 2026-05-31T07:41:19.299Z
updated: 2026-05-31T08:26:45.112Z
tags: ["aws", "data-science"]
reading_time: 6
upvotes: 0
comments: 0
language: en
---

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# Accelerate Mixtral 8x7B with Speculative Decoding and Quantization on Amazon SageMaker

**[Philipp Schmid](https://daily.dev/sources/philschmid)** · 6 min read · 0 upvotes · 0 comments

## Summary

A step-by-step guide to deploying Mixtral 8x7B on Amazon SageMaker using two optimization techniques: Medusa (speculative decoding) and AWQ (Activation-aware Weight Quantization). Combining both allows deployment on a single g5.12xlarge instance with 4x NVIDIA A10G GPUs, achieving ~50-60ms per token latency and reducing infrastructure costs by ~3x compared to a g5.48xlarge. The guide covers setting up the environment, retrieving the Hugging Face LLM DLC, preparing combined Medusa+AWQ artifacts, deploying the endpoint, and running inference.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.philschmid.de/sagemaker-awq-medusa>

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---

Tags: [#aws](https://daily.dev/tags/aws), [#data-science](https://daily.dev/tags/data-science)

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