FortaifyLocal LLM guide
Choose a local language model that fits the job and the machine.
A local LLM runs inference on hardware you control. The useful choice is rarely the largest available model; it is the model whose memory, speed, context, license, and task quality fit the actual workflow.
Browse GlyphSize for memory first
Model weights, context, cache, and application overhead all consume memory. Quantization can reduce the footprint, with a possible quality tradeoff. Leave headroom for the operating system and other applications instead of treating published weight size as total RAM usage.
Evaluate the complete model
Check the model license, source, architecture support, context window, prompt format, and quantized artifact. Test the tasks that matter to you, including failure cases. Public benchmark scores are useful context but cannot substitute for testing your own documents and prompts.
Use the right runtime
Glyph can work with its supported local runtime and customer-managed OpenAI-compatible endpoints. Ollama, llama.cpp-based services, and LM Studio serve different operational preferences. Confirm the exact API and model configuration before a team rollout.