Based on this YouTube video's total view count of 24.3K views and industry-standard rates, the estimated total earning is $17 - $49 through ad revenue. Historical data is not yet available to calculate daily, weekly, or monthly averages.

Explore Running a 22GB AI Model on a 6GB GPU, FAST (llama.cpp Guide) with 24,299 views, 545 likes, and 44 comments. Experience the impact of this video content that has captured audience attention.

In this deep-dive tutorial, we explore how to run the Qwen3.6-35B-A3B Mixture of Experts (MoE) model on a standard 6GB VRAM GPU using llama.cpp by leveraging CPU offloading and memory bandwidth architecture. By properly tuning five critical flags—including --n-cpu-moe to park idle routed experts in system RAM and optimizing the --load-mode (mmap vs mlock) scheduler—you can achieve 17 tokens per second on a 10-year-old GTX 1060 without buying expensive AI hardware. 🔔 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 We break down the exact memory traffic mechanics of sparse models versus dense LLMs, and why the "VRAM wall" is now just a budget problem. We also explore the harsh realities of speculative decoding thrashing the memory bus on MoE architectures, how TurboQuant drastically reduces Key-Value (KV) cache size using 3-bit quantization, and why beginner tools like Ollama and LM Studio hide the exact micro-batch tuning you need to unlock local coding agents. If you found this technical breakdown on local LLMs and hardware optimization helpful, drop a like and subscribe for more deep dives into software architecture and AI systems! ⏱️ TIMESTAMPS: 00:00 - The 35B Model on a 6GB GPU 00:28 - Qwen3.6-35B & MoE Architecture Explained 01:16 - Dense Models vs Sparse Memory Traffic 01:52 - Flag 1: The Magic of --n-cpu-moe 03:08 - Flags 2, 3 & 4: Mmap, Mlock, and GPU Layers 04:31 - The System RAM Bandwidth Bottleneck 05:12 - Fixing Prompt Processing (Micro-Batch Tuning) 06:45 - The Context Problem & TurboQuant (3-Bit KV Cache) 09:09 - Why Speculative Decoding Fails on MoE Models 10:16 - 4-Bit Quantization Quality & Final Verdict #llamacpp #localllm #machinelearning #gpu #qwen #artificialintelligence #systemdesign #softwareengineering User Queries: how to run 35b model on 6gb vram local llm llama cpp n-cpu-moe flag explained qwen 35b mixture of experts vram requirements how to optimize llama cpp for slow old gpu turboquant kv cache llama cpp pull request why is speculative decoding slow on moe models ollama vs lm studio custom expert offloading mmap vs mlock vs load mode llama cpp local coding agent system ram vs vram bandwidth mixture of experts vs dense llm memory traffic

With SocialCounts.org’s view counter, track your YouTube video’s live view count and YouTube likes count in real time with fast, reliable updates.

Watch every YouTube video live view count rise with our real-time YouTube views tracker—built for accuracy and minimal delay.

Follow YouTube real time views as they happen, using our dedicated view counter for YouTube videos.

Get up-to-date live view count on YouTube and see real-time growth with SocialCounts.org’s smart tracking tools.