# I Tested Every Best Local AI For GPU (8GB TO 24GB VRAM) **URL:** \ **Category:** Content Creators\ **Tags:** gpu, machine-learning, kai, hardware, running-locally, tech, quantisation\ **Created:** [21 October 2026 10:30 UTC](https://www.artofsm.art/t/i-tested-every-best-local-ai-for-gpu-8gb-to-24gb-vram/26541 "2026-10-21T10:30:33Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [21 October 2026 10:30 UTC](https://www.artofsm.art/t/i-tested-every-best-local-ai-for-gpu-8gb-to-24gb-vram/26541/1 "2026-10-21T10:30:33Z")
[![](https://www.artofsm.art/uploads/default/original/3X/b/9/b92e7b105d6270245bb16b28d3de211808d860f6.jpeg "I Tested Every Best Local AI For GPU (8GB TO 24GB VRAM)") ](https://www.youtube.com/watch?v=ugKAkCVBcz0) The video explains that the performance of local AI models depends not just on GPU VRAM size but critically on the specific model file used, highlighting differences between dense and mixture of experts (MoE) models and recommending optimized files for various GPU tiers. It advises users to consider system RAM, choose models tailored to their hardware, and use benchmarking tools to avoid slowdowns and unnecessary upgrades, emphasizing that model file quality often outweighs model size or bit precision. ---
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [21 October 2026 10:51 UTC](https://www.artofsm.art/t/i-tested-every-best-local-ai-for-gpu-8gb-to-24gb-vram/26541/3 "2026-10-21T10:51:00Z")
The video begins with the creator sharing a personal story about the frustration of running a local AI model on their laptop, which took two and a half hours to generate a single page of output. This delay was due to using the correct model name but the wrong file, a common mistake not addressed by popular tier lists that recommend models based solely on GPU VRAM size. The creator explains that while these tier lists correctly note that model size must fit within GPU memory to avoid severe slowdowns, they overlook the critical importance of the specific model file used, which can drastically affect performance. A key insight discussed is the difference between dense models and mixture of experts (MoE) models. Dense models require the entire model to fit into GPU VRAM for optimal speed, while MoE models, like Quen 3.635B, have many parameters but only activate a subset per token, allowing parts of the model to reside in system RAM without crippling performance. This explains how some larger models can run faster on GPUs with less VRAM if they leverage system memory effectively, challenging the conventional wisdom of simply matching model size to GPU VRAM. The video then provides detailed recommendations for different GPU VRAM tiers. For 8GB cards, having sufficient system RAM (around 32GB) is crucial to run MoE models efficiently, while smaller dense models like Gemma 4 12B are better for chat tasks. For 12GB GPUs, the creator highlights the importance of choosing the right file version of Quen 3.827B, noting that some files with the same bit precision differ significantly in quality due to how bits are allocated across the model. For 16GB cards, the default large files often do not fit entirely in VRAM once system overhead is considered, causing slowdowns; instead, smaller, smarter compressed files like ISTA’s 3.5-bit build are recommended. At the 24GB VRAM level, models like Quen 3.827B at Q4 compression fit comfortably with room for large context windows, providing good performance. The video also introduces Nvidia’s new Neotron 3.5 Bolt model, which uses advanced compression and training techniques to fit a 74 billion parameter model into 24GB VRAM, showing promising benchmarks but still requiring more real-world testing. The creator cautions that token generation speed and actual task completion time can differ significantly due to reasoning overhead, and hardware thermal limits can also impact sustained performance. In conclusion, the video emphasizes that when selecting a local AI model, the specific file and its creator matter more than just the model name or bit precision. Users should start with their existing GPU, check system RAM, and choose files optimized for their hardware and workload. The creator encourages viewers to avoid the common mistake of assuming slow performance is normal and recommends using tools like Chat LLM by Abacus AAI to benchmark local models against hosted APIs to ensure they meet real-world needs. This approach helps users save money and avoid unnecessary hardware upgrades.