Understand the estimate.

llm4fun combines model and hardware specifications, documented assumptions and available calibration data to estimate generation speed and memory use. A point on a graph is a central estimate; a displayed range represents uncertainty in the model, not a statistical confidence interval or guaranteed minimum.

Actual results depend on inference software, backend support, operating system, drivers, kernels, quantisation, context length, prompt length, batching, concurrent users, power limits, cooling and memory allocation. A product sold with the same chip may perform differently from the reference profile.

Published benchmark observations are separate from simulated results. Even a measured result applies to its recorded hardware, software and workload; it is not a guarantee for another configuration. Validate an important purchase with representative benchmarks on the intended setup.

“Fits” describes the estimated memory requirement under the selected settings. It does not promise software compatibility, reliability, output quality or useful performance for every task. CPU offload can let a model fit while making it much slower.

Quantisation can alter accuracy. Longer context increases memory requirements and can change speed; exceeding a model’s native context limit is not evidence that it will work well. Unsupported estimates and alternate precision choices need particular care.

Model size and parameter count are not quality scores. A local model, an API model with a similar name, and a frontier-model subscription can have different capabilities, precision, limits and included features.

Hardware prices, cloud rates, subscription prices and currency conversions are sourced snapshots or values you enter. They can become outdated and may exclude taxes, shipping, storage, network charges or other costs. Check the provider’s current quotation and terms.

The ROI calculation projects the selected purchase price, electricity settings, workload, usage and cloud pricing forward. Break-even means the two projected cumulative costs are equal. It does not promise a financial return, profitable work, resale value or that today’s hardware and prices will remain relevant for that long.

The token burn estimate uses the selected generation rate and utilisation. A full-time scenario assumes 24/7 availability, expressed as 730 hours per average month. Prompt processing, maintenance, downtime, cold starts and rate limits can reduce real output. Electricity can be excluded for exploration; doing so does not mean operating the computer uses no energy.

Hardware replacement, financing, maintenance, future price changes, changing model needs and resale value are not automatically included. Long break-even periods are particularly sensitive to those omissions. Subscription equivalents show a spending comparison, not equivalent model capability or an interchangeable service.

Importing a product page produces a draft. Pages can describe multiple configurations, optional upgrades, regional prices or incomplete specifications. Confirm the exact GPU, memory and price before using it.

Reviewing an off-the-shelf computer for the shared catalog does not constitute a purchase recommendation, vendor endorsement or performance certification. New custom builds remain temporary to the simulation. A vendor’s “AI PC” label or NPU TOPS figure alone is not enough to estimate LLM performance.

Use the simulator to narrow your options and understand trade-offs. Confirm compatibility, software support, capacity, price and measured performance before spending money or relying on a system for production work.

The website does not execute purchases, run the selected AI model or provide personalised financial advice. To report a suspected error, include the hardware, model and scenario at support@llm4fun.com. Please avoid sending confidential information.