Independent product intelligence · Worldwide · 2026-05-01 → 2026-07-31
No product description has been published for this domain yet. Everything below is measured directly.
Traffic & engagement
Traffic and engagement were not measured for this domain. The ranking positions below were.
Search demand
Search economics, brand dependence and ranking depth expose whether demand is portable or tied to the brand.
| # | Keyword | Clicks | Organic | Paid | Share |
|---|---|---|---|---|---|
| 1 | whisper.cpp | 1.5K | 1.5K | 0 | 52.20% |
| 2 | wisper.ggml download | 340 | 340 | 0 | 11.53% |
| 3 | brantôme 81 (1982) subtitles | 150 | 150 | 0 | 5.08% |
| 4 | whisper cpp | 140 | 140 | 0 | 4.75% |
| 5 | whisper web | 110 | 110 | 0 | 3.73% |
AI referral visibility
AI referrals are still a minority channel, but they show which assistants and pages are becoming discovery surfaces.
The identifiable AI mix includes ChatGPT, Claude, while the source groups 86.9% into “Others”.
| # | Page / folder | Traffic | Share |
|---|---|---|---|
| 1 | other | 57.3K | 86.89% |
| 2 | ggml.ai/whisper.cpp | 8.5K | 12.84% |
| 3 | ggml.ai/whisper.cpp/stream | 179.8 | 0.27% |
Product profile
Traffic makes more sense when you can connect it to the actual product promise.
“GGML”
No usable description reached this profile from any discovery source, so none is shown. The measured signals below are unaffected.
4 of 8 signal groups are available for this product: identity, authority and backlinks, AI referrals, search demand.
Not measured for this domain: traffic and engagement, audience demographics, country distribution, acquisition mix. Those sections are left out of the page rather than filled with estimates.
Canonical website: https://ggml.ai/
Observed across 2 discovery sources.
Research questions
Every answer is tied to a source we collected, and the questions with no evidence behind them are left unanswered rather than guessed.
GGML offers a low-level cross-platform implementation with integer quantization support, broad hardware compatibility, and no third-party dependencies. It is designed for zero memory allocations during runtime, emphasizing a minimal and simple codebase under an MIT license.
Measured traffic data shows an average of 2,417 monthly search visits from May to July 2026. The top search terms driving this traffic include 'whisper.cpp' (1,540 clicks), 'wisper.ggml download' (340 clicks), and 'whisper cpp' (140 clicks).
GGML received a total of 65,953 AI traffic visits from February to July 2026. ChatGPT is the primary referrer, accounting for 8,628 visits or 13.08% of the total AI traffic, while Claude contributed a negligible 19 visits.
Search traffic for GGML grew by 36.8% from 2,009 monthly visits in May 2026 to 2,749 monthly visits in July 2026. The site's referring domains also showed a steady increase, rising from 445 on August 12, 2026, to 459 by August 23, 2026.
GGML's domain authority is 23, which is slightly above the median of 22 for products in the Developer & AI Platform category. Its measured AI traffic of 65,953 visits significantly exceeds the category median of 0.0 AI traffic visits.
GGML has a domain authority score of 23. Measured data shows it has 459 referring domains and 3,485 total backlinks as of August 23, 2026.
GGML is a tensor library for machine learning that enables large models and high performance on commodity hardware. It provides a low-level, cross-platform implementation with integer quantization support and broad hardware compatibility to simplify running AI models locally.
GGML is used as the underlying library for projects like llama.cpp and whisper.cpp, enabling large language and speech-to-text models to run efficiently on consumer hardware. It is applied in situations requiring high-performance, low-level AI inference at the edge.
GGML is for developers and engineers building or deploying AI applications, particularly those working on models that need to run on local, commodity hardware with minimal dependencies. It is a foundational tool for projects in the developer and AI platform category.
GGML is a tensor library for machine learning designed for AI at the edge, simplifying the deployment of large models on commodity hardware. It is an open-core project founded in 2023 and now part of the Hugging Face ecosystem.
Behind the lock
Worldwide estimates · Data period 2026-05-01 → 2026-07-31 · AI referral period 2026-02-01 → 2026-07-31. Figures are measured estimates intended for market research. Signals we have not measured for this domain are omitted, never estimated; a measured zero is still reported as zero. How this was measured