
How Much VRAM for a 7B Model? Quantization and Context
Size a 7B model using documented memory figures for BF16, INT8 and INT4. Account for KV cache, context length…

Qwen3.5-9B is the best local LLM for 16GB RAM, Gemma 4 12B the runner-up. KV cache math, fit checks, and when gpt-oss-20b is worth it.
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Size a 7B model using documented memory figures for BF16, INT8 and INT4. Account for KV cache, context length…

A practical comparison of LM Studio and Ollama for local LLMs: licensing limits, OpenAI API coverage, and the…

Compare GGUF and Safetensors for inference, training, quantization and LM Studio support, including when MLX …

LM Studio system requirements for Windows, Mac and Linux: RAM and VRAM recommendations, CPU support, GPU offl…

LM Studio has no official Unraid app. The three real options for headless GPU inference on a server, what eac…

How GGUF quantization trades model quality for memory, what Q4_K_M costs you against Q8_0, and how to match a…
Running a language model on your own machine comes down to three decisions in order: whether the hardware can hold the model, whether the model exists in a format LM Studio can open, and which quantization of it to run. These cover all three.
Estimate the memory a model needs before downloading it. Runs in your browser.
The hard platform floors, and how to size a machine to the model you want.
What Q4_K_M actually costs you against Q8_0, and how to choose between them.
Why a model will not load, and how to find a build that will.
Serving models from a home server instead of a desktop? Read the headless and Unraid options.