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Explore Best Local AI Models for Every VRAM Tier (4GB to 32GB+) with 36,460 views, 791 likes, and 103 comments. Experience the impact of this video content that has captured audience attention.

With 16GB VRAM officially taking the #1 spot on the Steam Hardware Survey, which local AI models should you actually run on 4GB, 6GB, 8GB, 16GB, 24GB, and 32GB+ GPUs? Meet the definitive 2026 local LLM tier list and the 22.2-point harness discovery that changes everything. 🔔 Subscribe: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w?sub_confirmation=1 💙 Become a Member: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w/join 🐦 Twitter/X: https://x.com/cloud_codes 💬 Discord: https://discord.gg/4kJqEBMMf In this deep dive, Cloud Codes breaks down the memory arithmetic, quantization math (Q4_K_M at 4.8 bits/weight), and benchmark rankings across every consumer VRAM budget. We analyze Microsoft's Phi-4 mini for 4GB cards, explain why 6GB users must stop running 2023 7B models (as Mistral Small 4 exploded to a 119B MoE), review Qwen 3.5 9B's 81.7 GPQA Diamond score beating 120B models on 8GB cards, and contrast Qwen 3.6 35B MoE (100 tok/s) vs Qwen 3.6 27B Dense on 16GB and 24GB VRAM GPUs. Furthermore, we dissect Qwen 3 Coder Next (80B MoE) for 32GB+ workstations streaming idle experts from RAM, audit the Artificial Analysis Index v4.1 local-vs-cloud performance gap, and reveal independent research proving identical weights jump 22.2 points on SWE-bench Verified (from 67.8% to 90.0%) purely by upgrading the agent harness wrapper. If this helped you understand backend architecture, system design, and how to build faster software, subscribe to Cloud Codes for a new infrastructure breakdown every single week! Build, solve, deploy. 🔗 Repositories & Sources Mentioned: Microsoft Phi-4 Mini: https://huggingface.co/microsoft/Phi-4-mini-instruct Google Gemma 4 E4B: https://huggingface.co/google/gemma-4-e4b-it Qwen 3.5 9B: https://huggingface.co/Qwen/Qwen3.5-9B Qwen 3.6 27B: https://huggingface.co/Qwen/Qwen3.6-27B Qwen 3 Coder Next 80B: https://huggingface.co/Qwen/Qwen3-Coder-Next Ollama: https://github.com/ollama/ollama LM Studio: https://lmstudio.ai/ Artificial Analysis: https://artificialanalysis.ai/ ⏱️ Video Chapters: 0:00 - The Steam Hardware Shift: 16GB VRAM Takes #1 1:07 - The Quantization Math: Q4_K_M Arithmetic 3:21 - 4GB VRAM Tier: Microsoft Phi-4 Mini (3.8B) 5:25 - 6GB VRAM Tier: Why You Must Stop Using 2023 7B Models 7:23 - 8GB VRAM Tier: Qwen 3.5 9B (Beating 120B Models) 10:41 - 16GB VRAM Tier: Qwen 3.6 35B MoE (100 tok/s) 12:08 - 24GB VRAM Tier: Qwen 3.6 27B Dense (Single RTX 3090/4090) 13:40 - 32GB+ Workstation Tier: Qwen 3 Coder Next (80B MoE) 15:03 - Artificial Analysis Index v4.1: Local vs Frontier Gap 17:32 - The 22.2 Point Discovery: Why Harness Beats VRAM Tier 18:52 - Final Summary: The Complete Local AI Buying Ladder #localai #ollama #lmstudio #qwen #gpus #vram #systemdesign #cloudcodes #machinelearning #softwareengineering User Queries: best local ai models for 8gb vram 2026 best local llm for 16gb vram qwen 3.6 35b moe phi 4 mini vs gemma 4 e4b 4gb vram local ai qwen 3.5 9b gpqa diamond score vs gpt oss 120b qwen 3.6 27b dense vs 35b moe speed benchmark qwen 3 coder next 80b moe workstation requirements q4_k_m quantization memory calculation 4.8 bits per weight artificial analysis intelligence index v4.1 local llm swebench verified 90 percent harness mrguo6221 cloud codes best local ai models vram breakdown

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