标签：#minipc

For decades, desktop computers followed a familiar formula: a large case, powerful components, dedicated graphics cards, and plenty of space for upgrades. But the way we use computers is changing. Today, many users are looking for something different: a computer that is powerful enough for their daily needs, consumes less energy, takes less space, and can adapt to modern workflows. This is where Mini PCs are becoming one of the most interesting trends in personal computing. What is a Mini PC? A Mini PC is a compact computer designed to provide desktop-like functionality in a much smaller form factor. Unlike traditional desktop towers, Mini PCs integrate most components into a small chassis while still offering modern performance. A typical Mini PC includes: Modern processors from AMD or Intel Integrated Radeon or Intel graphics RAM and SSD storage Multiple connectivity options Compact cooling solutions Companies such as Minisforum have helped accelerate this trend by creating small computers powered by modern Ryzen and Intel processors, showing that compact hardware can still deliver impressive performance. Why are Mini PCs becoming popular? Efficiency matters more than ever One of the biggest advantages of Mini PCs is their efficiency. Traditional desktop computers can require significant power depending on the hardware configuration. In comparison, many Mini PCs provide enough performance for everyday tasks while maintaining lower energy consumption. For many users, reducing power usage without sacrificing productivity is becoming increasingly important. Small computers, new possibilities A smaller computer changes how we think about desktop setups. Mini PCs can be used for: Software development environments Home servers Media centers Student workstations Office computers Compact gaming setups A powerful computer no longer needs to occupy a large space on or under your desk. Modern processors changed the game The biggest reason Mini PCs are becoming more capable i

Did you know that a 35-billion-parameter model can generate tokens at the same compute cost as a 4B model? That single fact made me abandon a multi-model agent architecture I'd spent a weekend building. But I had to run the benchmarks first to understand why. Here's the full breakdown, with commands, numbers, and the architectural reason it all falls apart on shared-memory hardware. The Discovery That Changed Everything I'd been running qwen3.6:35b on my Minisforum UM790Pro for weeks as my daily coding assistant. 17.8 tokens/second -- genuinely usable for interactive work. But I kept wondering: could I run a lightweight sidecar model alongside it for quick classification and tool-calling in an agent pipeline? Before I even started benchmarking, I dug into what qwen3.6:35b actually is under the hood. It's a Mixture of Experts model: 256 total experts with only 8 activated per token. The architecture also incorporates SSM (State Space Model) components alongside traditional attention -- Mamba-style layers that handle certain sequence patterns more efficiently than pure transformers. The math hit me: 8 out of 256 experts means each token only touches roughly 4-5B parameters worth of compute. The model carries 36 billion parameters of knowledge , but its per-token cost is comparable to a small dense model. I was planning to run a separate 4B model for "fast tasks" next to a model that already operates at 4B-class speed. But I had to prove it with numbers. Hardware and Ollama Setup The UM790Pro specs that matter for this experiment: CPU: AMD Ryzen 9 7940HS (Zen 4, 8C/16T) iGPU: AMD Radeon 780M (12 RDNA 3 compute units) RAM: 96 GB DDR5-5600 (~80 GB/s bandwidth) GPU memory pool: 2 GB dedicated VRAM + 46 GB GTT = 48 GB GPU-accessible That 48 GB GPU pool sounds enormous until you realize it's carved from the same DDR5 that the CPU also uses. There is no separate GDDR6 bus. Everything -- CPU inference, GPU inference, KV caches, OS operations -- flows through one 80 GB/s pipe.