What Thinking Mode Actually Costs You (September 2026)
Reasoning tokens, latency, and KV cache cost of thinking mode in local LLMs. How to toggle it per runner and when it is the wrong choice.
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#qwen3
How much context can a local LLM actually hold? (September 2026)
The advertised context window is one number. What your GPU can actually serve is smaller. The KV cache math, RoPE scaling, and the practical ceiling.
Run an LLM Locally on a Raspberry Pi (September 2026)
Raspberry Pi 4 and Pi 5 can run small open-weight models on CPU alone. What fits on 2 GB, 4 GB, 8 GB, or 16 GB of RAM, and what speed is realistic.
How much VRAM does a local LLM actually need? (August 2026)
The published GGUF file size is the weights. VRAM use also includes the KV cache, framework overhead, and your context length. The math, walked through.
Small Models, Big Brain: When 4 Billion Parameters Match GPT-4
Modern sub-10B models now rival last year's frontier AI on reasoning, tool use, and code. The benchmarks prove it.