0xSero Shares Workflow on Using Sparks for Model Quantisation and Execution

X post0xSero28 September 2026Tutorial

In a short video post, 0xSero outlines their workflow for using Sparks, covering how to quantise, test, and run models, as well as handling different memory types.

Key points

Process for quantising models using Sparks
Approaches to testing and running local models
Overview of different types of memory involved

Why it matters

Practical workflows for quantising and serving local AI models provide useful operational reference points for hardware resource management.

Analysis

The source is concise and centres around a shared video guide. 0xSero focuses on practical execution details on Sparks hardware, specifically targeting quantisation workflows and memory considerations.

Original post

0xSero

@0xSero · 28 September 2026

Here's how I'm using the Sparks. - How I quantise - How I test models - How I run models - Different types of memory t.co/BLimNqMqdS

275 likes6 replies304 bookmarks14.1k views

View on X

Selected replies (5)

@CtrlAltBelieve @0xSero i’m itching to turn my Spark into Spark(s) lol
@lucaxyzz @0xSero Wow this is doxxed sero?
@NoPowerPoints @0xSero RTX SPARK will be interesting to see
@GPUinf @0xSero Sparks how-to still burns a GPU-hour. Rental desk sells what's racked.
@cozybearlog @0xSero My lazy first check on any quant claim is still params times bits divided by 8, just to know the floor. With the 16 bit sliding scales on top, how far above that floor do your exl3 builds usually land?

Mentioned links

Post by 0xSero on Xx.com/0xSero/status/2104602043097002275

Summary generated by AI (Gemini 3.8 Flash, 29 September 2026). It may contain errors – the original source is authoritative.

Similar in content, found by the AI search.

X post:0xSero24 Sept 2026
0xSero Showcases Minimalist Interface for Local AI
Developer 0xSero has previewed a new local AI application focused on a clean, minimal, and intuitive user interface. Technical details in the initial post remain brief.
164bookmarks21.4kviews
AI & LLMs· Announcement
X post:Tech2Wild7 Aug 2026
Optimizing Dual GPU Setups for Large Language Models
This post explores a technical setup for running large language models using dual GPU configurations, specifically highlighting the use of hardware like the Spark and dual 4090s. The author provides a detailed guide on managing context windows and model instances to improve performance and efficiency for local AI workflows.
52bookmarks8.7kviews
AI & LLMs
X post:0xSero27 Jul 2026
LLM Benchmark Performance at 3-Bit Quantisation
A post on X highlights surprisingly high benchmark performance for a language model running at 3-bit quantisation (exl-3bits), achieving a 78.6% score alongside 100 tokens per second at 1400W peak power.
36bookmarks11kviews
AI & LLMs· Discussion
Article:Inferact5 Sept 2026
vLLM on NVIDIA DGX Spark: Architecture, Configuration, and Local Evaluation
This guide details running vLLM on NVIDIA's DGX Spark (GB10) desk-side workstation. Leveraging its 128 GB unified memory pool, the system is designed for local inference with NVFP4 MoE models such as Nemotron-3-Super.
AI & LLMs· Tutorial
Repository:MiaAI-Lab/DeepSeek-v4-Flash-One-DGX-Spark7 Sept 2026
Running DeepSeek-v4-Flash on a Single DGX Spark
An open-source launcher and Docker recipe for running DeepSeek V4 Flash 0731 (EXL3) on a single NVIDIA DGX Spark with 128 GiB unified memory.
328starsPython
AI & LLMs· Tool
Video
Video:Heavy Metal Cloud29 Jun 2026
Nvidia DGX Spark Hands-On: Setup, vLLM Serving, and Mac Studio Comparison
Heavy Metal Cloud reviews the Gigabyte and ASUS Nvidia DGX Spark desktop units powered by the Grace Blackwell architecture. The video provides a step-by-step guide for local LLM serving using Docker, vLLM, and SparkRun, as well as media generation in ComfyUI, comparing real-world performance against a Mac Studio.
AI & LLMs· Tutorial