What is this?
LocalLLM Advisor is a free tool that helps you find the best Large Language Model for your specific hardware, or the best hardware for your LLM, based on your specific configuration and needs. Instead of guessing whether a model will run on your GPU, you get concrete estimates based on real specifications, and instead of buying hardware based on assumptions, you can make informed decisions based on your specific use case.
Why we built it
Running LLMs locally is becoming increasingly popular, but choosing the right model is confusing. You need to consider VRAM, memory bandwidth, quantization levels, and how these affect both quality and speed. Most people either pick a model that is too big (and runs painfully slow) or too small (missing out on better quality). Moreover, buying new hardware is a big investment, and it's hard to know what will work best for your needs.
We wanted a tool that gives honest, data-driven recommendations, not marketing hype.
Ethical AI by Design
Running AI locally is not just a technical decision, it is an ethical one. The mainstream narrative around AI has largely normalised the idea that to use capable AI tools, you must hand over your data to a third party. We think that trade-off is neither necessary nor acceptable as a default.
When a model runs on your own hardware, several concrete ethical problems disappear: your conversations cannot be used to train future commercial models without your consent, no company builds a behavioural profile from your queries, and sensitive topics (health, legal matters, personal relationships, business strategy) stay on your device by architecture, not merely by policy. Data sovereignty is not a marketing promise; it is a technical reality.
Open-source models running locally represent one of the most tangible answers the AI community has produced to questions about privacy, autonomy, and accountability. We built this tool because we believe capable AI and respect for user rights are not in conflict.
How it works
We combine three data sources:
- Hardware specs database: 50+ GPUs and 30+ CPUs with detailed specifications (VRAM, bandwidth, compute performance)
- Model benchmarks. Data from the Open LLM Leaderboard on HuggingFace, including IFEval, BBH, MATH, GPQA, and more
- Performance formulas. Physics-based calculations for token generation speed, VRAM usage, and inference modes
For the full technical details, see our Methodology page.
Limitations
Our estimates are approximations based on theoretical calculations. Real-world performance depends on many factors: your specific system configuration, the inference engine you use (llama.cpp, Ollama, vLLM), background processes, and more.
We are constantly improving our models. If you find significant discrepancies between our estimates and your real-world results, please let us know at [email protected].
No Affiliation
LocalLLM Advisor is an independent project. We are not affiliated with Ollama, HuggingFace, NVIDIA, AMD, Apple, or any model provider. Our recommendations are based purely on data, not sponsorships.