Analysis & Reports
In-depth hardware analysis, model comparisons, and industry reports from the RunThisModel research team.
Best GPUs for Running AI Models Locally in 2026
A comprehensive analysis of GPU options for local AI inference, from budget RTX 4060 to flagship RTX 5090. We break down VRAM, bandwidth, and price-to-performance ratios.
Stable Diffusion vs Flux: Hardware Requirements Compared
Flux.1 demands significantly more VRAM than SDXL. We analyze the exact requirements, quantization options, and which GPU tiers can handle each model.
The Complete Guide to Running LLMs Locally in 2026
Everything you need to know about running AI models on your own hardware. From choosing the right model and quantization to setting up Ollama and optimizing performance.
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Cloud GPU Showdown: RunPod vs Vast.ai for AI Inference
When your local hardware falls short, cloud GPUs fill the gap. We compare pricing, availability, ease of use, and performance across the top providers.
Whisper Model Sizes: Which One Should You Use?
OpenAI Whisper comes in 6 sizes from Tiny (39M) to Large-v3 (1.55B). We analyze accuracy, speed, and VRAM trade-offs to help you pick the right one.
AI Video Generation Hardware Requirements: CogVideoX, Mochi & Wan Compared
Video generation is the most VRAM-hungry AI task. We analyze what hardware you actually need for CogVideoX, Mochi 1, and Wan 2.1.
Running AI Models on Apple Silicon: M1 Through M4 Ultra
Apple Silicon's unified memory architecture makes it uniquely suited for large models. We map every chip to its maximum model capacity.
GGUF Quantization Explained: Q4, Q5, Q8, and FP16
Quantization reduces model size and VRAM usage at the cost of quality. We explain each format, when to use it, and the real-world quality differences.
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