Content Policy & Editorial Standards
This page explains where the numbers on Local AI Master come from, what we have and have not measured ourselves, and how we correct mistakes. The short version: we own one machine we benchmark on and have measured one rented GPU, so most speeds on this site are calculated ceilings or figures quoted from a named source, and every one of them is labelled as such.
Human-Edited, Sourced, and Labelled
Every page on Local AI Master is:
- ✓Edited by a named person: the courses and guides are written and edited under the editor credited at the foot of each page (see About)
- ✓Measured only where we say so: our own speed measurements are published, with every device, host, Ollama version and date, in the open Local AI Benchmarks dataset (today our own Apple M3 Pro, 18 GB, and a rented NVIDIA L4, 24 GB); everything else is a calculated ceiling or a cited figure from a vendor's published specifications or another named source
- ✓Checked against the live registry: every Ollama pull or run tag we publish is checked to exist in the Ollama library before it goes on a page
- ✓Re-dated when refreshed: when a page's facts are updated, its “last updated” date changes with them
- ✓Sourced or shown: every performance number is either attributed to a named source or derived with the arithmetic printed next to it
What hardware we actually have
We do not own a GPU lab, and you should be suspicious of any site that says it owns one of everything. What we have is:
- • An Apple Silicon Mac (M3 Pro, 18GB unified memory). It is the one machine we own and benchmark on. The current table was run with Ollama 0.40.13 on 2 November 2026, seven runs per model, and is published as a measured table on model pages such as Llama 3.1 8B and Mistral 7B. An earlier run (Ollama 0.12.3, October 2026) stays in the dataset with its own date.
- • A rented NVIDIA L4 (24 GB, 300 GB/s) on Google Cloud. We do not own it. Its rows were run with Ollama 0.40.13 on 3 November 2026, seven runs per model, and a rented-GPU row is published only when two separate hosts agree on it.
That is the whole list. It means that, apart from the L4 rows, the per-card figures on this site are not ours: they are vendor-published specifications (we cross-check against the TechPowerUp GPU Database), results published by a named third party that we link to, or arithmetic we show our working for. We label which is which on the page, and we would rather tell you a number came from NVIDIA's spec sheet than pretend we measured it.
Where Each Number Comes From
Every speed or performance figure on the site is one of three things:
- • A labelled ceiling. Generating each token means reading the whole model from memory, so memory bandwidth divided by model size gives an upper bound. Example: a GPU with 1,008 GB/s of bandwidth running a 4.92 GB model file (llama3.1:8b) has a ~205 tok/s ceiling. Real speeds land below it. We take the bandwidth from the vendor's spec page and the file size from the Ollama library.
- • A measured row from our benchmark dataset, with the device, Ollama version, run count and date stated.
- • A cited figure from a vendor page, model card, paper or public leaderboard, with the source named and dated.
Mixture-of-experts and diffusion models get no tok/s figure at all, because the simple ceiling does not describe them.
What We Do Not Claim
To be specific about the limits of first-hand work on this site:
- • Apart from the rented NVIDIA L4, we have not benchmarked NVIDIA, AMD or Intel GPUs ourselves. Other GPU speeds are ceilings or cited figures.
- • We do not publish our own quality scores, ratings or “% of GPT” comparisons. Quality claims cite public benchmarks.
- • We do not publish invented reader stories or savings figures. Reviews on /reviews are submitted by learners and published only with their permission.
- • Earlier versions of this page described a large private dataset and rented-GPU testing. We removed those claims on November 2, 2026 because we could not document them. The L4 rows from November 3, 2026 are new runs, each one listed in the dataset with its host, Ollama version and date.
AI Disclosure Standards
Transparency is non-negotiable. Here is how AI tools are and are not used:
What a Person Is Responsible For
- • Tutorials: Every tutorial is reviewed and approved by the editor before it is published, and its commands follow the tool's official documentation
- • Model Pages: Our model pages are built from the model card, the vendor's published figures and independent benchmark results, each attributed on the page. We do not claim to have personally run every model listed
- • Technical Analysis: Performance claims are attributed to a named source, or derived with the arithmetic shown, never estimated and presented as measured
- • Model Tags and Commands: Ollama tags are checked against the live registry; commands are taken from official docs
- • Troubleshooting Guides: Built from the error text users report and the fixes documented by the tool's maintainers
Where AI Tools Are Used
- • Drafting and research assistance: AI tools help draft and research content, which is then checked against primary sources and edited. Our editorial policy carries the full AI disclosure
- • Grammar Checking: AI tools help catch typos
- • Image Generation: Some OG images are created with AI
- • Code Formatting: AI may suggest formatting for code examples
Promise: AI assistance never replaces the sourcing rules above. A number an AI tool suggests is not published unless it can be traced to a source or derived with shown arithmetic.
Editorial Independence
Local AI Master maintains strict editorial independence. Our recommendations and comparisons are based on published specifications, sourced benchmarks and price, never influenced by:
- • Sponsorships: We don't accept sponsored content or paid model placements
- • Affiliate Pressure: Affiliate links exist, but they never decide our recommendations
- • Vendor Relationships: Model creators don't get preferential treatment or advance reviews
- • User Pressure: Popular opinion doesn't override sourced data
Example: A model page such as DeepSeek Coder V2 is written the same way whether or not any product on the page earns a commission: from the model card, the Ollama library entry and published benchmarks.
Fact-Checking Process
Every technical claim is checked before publication:
Our 5-Step Fact-Checking
- 1. Primary Sources: Specs, context windows and licences come from the vendor page or model card
- 2. Registry Check: Every Ollama tag on the page is checked to exist in the Ollama library; file sizes come from the registry
- 3. Speed Labels: Each speed is checked to be a labelled ceiling, a row from our benchmark dataset, or a cited figure
- 4. Date Check: Prices, versions and release dates are re-read on the day a page is refreshed, and the page is re-dated
- 5. Reader Reports: Corrections sent by readers are checked against the source and fixed
Commitment: If a claim cannot be traced to a source or derived with shown arithmetic, it is not published.
Research Methodology
Our content creation follows a systematic research process:
For Model Pages
- • Read the model card and the developer's release notes
- • Confirm the Ollama tags and file sizes in the registry
- • Work out VRAM needs from file size plus context
- • Calculate speed ceilings from vendor bandwidth specs
- • Quote benchmark scores from named, dated sources
- • Compare against 3-5 similar models on the same basis
- • Link every source on the page
For Tutorials
- • Follow the tool's official install documentation
- • Note differences between Windows, Linux and macOS
- • Check every command and tag against current releases
- • Collect the error messages users report
- • Write troubleshooting from documented fixes
- • Update when the tool ships breaking changes
- • Re-date the page when it is refreshed
Timeline: A model page is not published until its claims are sourced and its tags exist in the registry.
Transparency About Limitations
We're honest about what we don't know and what we can't test:
Our Testing Limitations
- Hardware Constraints: We own no NVIDIA, AMD or Intel GPU for benchmarking. Apart from the rented NVIDIA L4 rows, GPU figures are ceilings or cited from official and third-party sources, not personal testing.
- Language Limitations: Content is written in English. Claims about non-English performance rely on the developer's published evaluations.
- Specialized Domains: Medical, legal and financial topics cite domain sources and are not professional advice.
- Enterprise Features: We do not run enterprise clusters. Deployment, clustering and cloud-scale sections cite official documentation.
- New Models: New releases are covered from the developer's published material first; the page says so.
Transparency Markers: Look for labels such as “ceiling”, “measured on Apple M3 Pro”, “rented NVIDIA L4”, or a named source next to a figure.
Sources and Citations
We cite authoritative sources to support claims:
- • Official Model Documentation: Direct links to model cards, research papers, and official repos
- • Academic Research: ArXiv papers, conference proceedings (NeurIPS, ICLR, CVPR)
- • Vendor Documentation: NVIDIA, AMD, Intel and Apple official specifications
- • Model Registry: The Ollama library for tags, quantisation and file sizes
- • Public Benchmarks: Hugging Face leaderboards, MLPerf results and similar, with dates
Citation Standard: Performance claims that are not ceilings or our own measurements must include a source link. Every measured row states the device, Ollama version, run count and date.
Corrections and Updates Policy
We fix errors transparently:
Correction Process
- Minor Typos/Grammar: Fixed without notice (doesn't affect technical accuracy)
- Technical Errors: Corrected and the page's “last updated” date changed
- Major Inaccuracies: The section is rewritten and the change noted on the page
- Breaking Changes: Tool or model updates that break a tutorial get updated instructions
Community Reporting: Found an error? Email support@localaimaster.com with “Content Correction” in the subject. Contributors who report errors can be credited (with permission).
Ethical AI Guidance
We promote responsible AI use and highlight ethical considerations:
Our Ethical Commitments
- Privacy First: Tutorials emphasize local deployment for data sovereignty. Cloud alternatives disclosed with privacy trade-offs.
- Bias Awareness: Model pages include known biases when the developer documents them.
- Environmental Impact: Power figures in hardware guides come from vendor specs. We recommend efficient models when appropriate.
- Legal Compliance: Licensing clearly explained. No guidance on circumventing model licenses or usage restrictions.
- Harm Prevention: No tutorials for generating deepfakes, impersonation, or deceptive AI content.
Stance on Controversial Use Cases: We provide technical education, not judgment. However, we won't create content specifically for surveillance, manipulation, or illegal activities. If a model has known misuse potential, we include responsible use warnings.
Community Feedback Integration
Your feedback shapes our content. Here's how we use reader input:
Reader Contributions
- • Error Reports: Checked against the source and fixed
- • Alternative Solutions: Added where they work for other readers
- • Hardware Variations: Folded into compatibility notes
- • Use Case Ideas: Inspire new tutorials
- • Benchmark Results: Third-party results can be cited with credit and a link
Pages Shaped by Common Questions
Recognition: Readers whose corrections change a page can be credited in that article, with their permission.
Conflict of Interest Disclosure
Full transparency on potential conflicts of interest:
Financial Relationships
- Affiliate Links: Some hardware links are affiliate links (for example Amazon). We earn a small commission at no cost to you. Recommendations are based on specs and price, not on which link pays.
- Courses: Local AI Master sells paid course plans. Free guides link to them, and that is disclosed by the plain fact of the link.
- Ads: If the site shows display ads, advertisers have zero influence on editorial content or model rankings.
- No Sponsorships: Local AI Master does not accept sponsored posts, paid reviews, or vendor partnerships that compromise editorial independence.
Promise: If any financial relationship ever influences content, it will be prominently disclosed at the top of the article. Your trust is more valuable than any commission.
Content Update Schedule
- Weekly: Review search data for pages that are slipping or out of date, and pick pages to refresh
- On new releases: Check new models in the Ollama library and update the pages that recommend models
- On refresh: Re-read prices, versions and tags on the day, and re-date the page
- As Needed: Fix breaking changes (framework updates, model deprecation, security issues) as they are reported
Last Major Audit: November 2, 2026. Site-wide cleanup: unsourced speeds replaced with labelled ceilings, invented testimonials removed, dead Ollama tags fixed, and this page rewritten to describe only what we can document. Previous audit: August 23, 2026, which removed unsourced performance claims and first-hand testing language across the hardware and troubleshooting clusters
Contact Us Directly
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Or use the contact page.
Written by
Barsha Mohapatra
Founder and Editor, Local AI Master · Founder, Little AI Master
Founder of Little AI Master and the editor behind every course and guide on Local AI Master. Years of tutoring students taught her where AI education actually fails.