Guides

Practical notes from setting up private AI: what hardware a model needs, which models and serving tools to use, and what it costs.

• NVIDIA DGX Spark for LLMs: what it runs

  What the DGX Spark can run: which model sizes fit its 128 GB of unified memory, how fast it generates, and when a company needs a GPU server instead.

• Hardware for local LLMs: GPUs, Macs and servers

  Which hardware runs local LLMs for a person, a team or a company: GPU memory, bandwidth, workstation cards, Macs, DGX Spark and GPU servers compared.

• Open-weight models for companies: how to choose

  What open-weight models are, how they differ from open source, the main model families and licences, and how a company picks one to run privately.

• Private ChatGPT for business: your options

  How companies get a private ChatGPT: enterprise plans, cloud deployments in your region or open-weight models on your own servers, and what stays private.

• RAG vs fine-tuning: which one your LLM needs

  When to use retrieval-augmented generation, when to fine-tune a model, and when to combine them, explained for teams building private LLM systems.

• vLLM vs Ollama vs LM Studio: which to use

  vLLM, Ollama and LM Studio compared for running local LLMs: who each tool is for, how they handle many users, model formats, APIs and production use.

Tools

• LLM VRAM calculator

  GPU memory for a model's weights and KV cache, by model size, precision, context length and users.

• LLM cost calculator: API vs self-hosted

  Monthly API spend against a rented or owned GPU server, and the volume where self-hosting breaks even.