ArticlesPrivate AI / Practical decisions
What to test, what to measure and what to ask before committing to private AI. Guides for the people planning, buying and running it.
/ Model suitability
For some internal workflows, yes. The decision depends on your documents, error tolerance and deployment, rather than whether a local model wins a public leaderboard.
Read the guide/ Inference engineering
A practical guide to expert offloading, PCIe bandwidth, system RAM and context memory, informed by Northloom’s Rust and CUDA inference engineering.
Read the guide/ Deployment costs
Compare private AI hardware with a cloud API using workload volume, useful output, power and operating time. A worked example shows where the financial decision changes.
Read the guide/ GPU memory
Estimate model weights, context memory and concurrent requests before buying a GPU. Worked examples explain why a model file that fits in VRAM can still run out of memory.
Read the guide/ Runtime selection
A practical comparison of local AI runtimes, from desktop tools to shared inference servers and specialist engines for models larger than GPU memory.
Read the guide/ Inference performance
A long wait before the first word and a slow stream of words are different problems. Separate them, measure a repeatable workload and investigate the stage that is taking the time.
Read the guide/ Model suitability
A convincing demonstration is a useful start. A small, structured evaluation tells you much more about whether a local model is ready for everyday work.
Read the guide/ Infrastructure planning
An AI server is only a good purchase if it can do your work at an acceptable speed. Start with the workload, then ask for evidence that the proposed system can handle it.
Read the guideAI Consultancy
Independent engineering advice on your proposed hardware, model suitability or inference performance. Defined assessments with written findings.
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