惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

大猫的无限游戏
大猫的无限游戏
aimingoo的专栏
aimingoo的专栏
I
InfoQ
B
Blog RSS Feed
D
DataBreaches.Net
S
SegmentFault 最新的问题
P
Proofpoint News Feed
A
About on SuperTechFans
WordPress大学
WordPress大学
Hugging Face - Blog
Hugging Face - Blog
博客园 - 司徒正美
小众软件
小众软件
博客园 - Franky
有赞技术团队
有赞技术团队
D
Docker
T
Tailwind CSS Blog
雷峰网
雷峰网
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Blog — PlanetScale
Blog — PlanetScale
酷 壳 – CoolShell
酷 壳 – CoolShell
B
Blog
V
Visual Studio Blog
宝玉的分享
宝玉的分享
爱范儿
爱范儿

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
Ollama 0.30 GPU Boost: Faster local Qwen inference on NVIDIA
EveryLocalAI · 2026-06-11 · via DEV Community

EveryLocalAI

This stack uses Ollama 0.30 to make desktop GPU inference faster. The latest Ollama release adds wider Vulkan/NVIDIA support, better GGUF compatibility, and a cleaner local GPU path for Qwen models.

What you get

  • Faster local inference on NVIDIA GPUs with Ollama 0.30
  • Improved GGUF model support for desktop workloads
  • A practical stack for Qwen 3.5 and Qwen 3.6 on high-end GPUs

Prerequisites

  • Desktop GPU such as RTX 4090 with Vulkan support
  • Latest NVIDIA drivers
  • Ollama 0.30 installed
  • At least 50 GB disk for model storage

Setup

curl -sSf https://ollama.com/install.sh | sh
ollama pull qwen3.5:9b
ollama pull qwen3.6:27b
ollama serve
ollama ps

If the model is still on CPU, update drivers and make sure Vulkan is enabled.

Use it

  • Local development with fast Qwen model responses
  • Desktop chat for privacy-first inference
  • GPU-powered research with a local model back end

Troubleshooting

  • GPU not used: check ollama ps and verify Vulkan support.
  • Model load errors: use GGUF models and update ollama to 0.30.
  • Unsupported model format: use GGUF or ollama pull with a supported model tag.

Originally published on https://everylocalai.com/stack/ollama-0-30-gpu-boost