Infrastructure & DevOps
OneTriangle
Last updated 2026-08-25 · benchmark measured 2026-11-07 — deterministic & reproducible
Managed LLM inference using KV cache transfer to reduce cost and latency.
OneTriangle at a glance
OneTriangle is a managed LLM inference service for ML teams and AI builders that speeds up and lowers the cost of prefill by running it on a smaller model and transferring its KV cache to a larger model for decoding.
Good for
- ML teams that want to reduce inference costs on long-context prompts
- LLM service providers looking to cut time to first token
- Cost-conscious AI builders using open-weight models like Llama, Qwen, Mistral, Gemma, or DeepSeek
Questions people ask
How does OneTriangle make inference cheaper and faster?
It runs the prefill stage on a smaller model and transfers its KV cache to a larger model for decoding, so the large model skips computing its own prefill and enters decode with a cache already populated.
What results has OneTriangle measured?
In one benchmark using Llama 3.1 8B for prefill and 70B for decode on a 100k-token context, time to first token dropped from 16.9s to 2.7s (6.3× faster) and prefill cost for 1M requests fell from $46.9k to $7.5k (84% cheaper).
Which models does OneTriangle support?
It supports open-weight models including Llama, Qwen, Mistral, Gemma, and DeepSeek.
How does OneTriangle make sure quality doesn't drop?
Each model pair is checked against held-out quality and latency tests before the KV cache transfer is applied; if a pair doesn't pass, the target model falls back to its normal prefill process.
How hard is it to integrate OneTriangle?
Teams change one line of configuration, and OneTriangle manages the GPU infrastructure and serving.
Written by Legit.Show from onetriangle.ai on 2026-09-30. It does not change the score.
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To cite this score: legit.show/s/onetriangle-ai/2026-11-07. That address never changes; this page moves with every re-measure.
Legit.Show scored OneTriangle 80/100 on 2026-11-07, measured across all 7 frames from its public surface. legit.show/s/onetriangle-ai/2026-11-07
Is OneTriangle production-ready?
Legit.Show scores OneTriangle 80 out of 100 — the simple average of its 7 measured frames. Legit.Show ran its deterministic 7-Frame production-readiness benchmark on OneTriangle (public-surface assessment), measured from the public surface with no LLM in the scoring path. Its strongest frame is Accessibility; its weakest is Privacy. Every frame it averages is published with its evidence on the Legit.Show listing.
The 7 Frames
- Performance — 99/100
- Accessibility — 100/100
- Security — 45/100How this compares across the catalogue: The Web Security Baseline
- Privacy — 25/100How this compares across the catalogue: The Privacy Gap
- Reliability — 100/100
- Standards — 100/100
- Discoverability — 90/100
What we measured
- Security headers present: HSTS.
- No Content-Security-Policy.
- Served over HTTPS with a valid certificate.
- Real Lighthouse performance run — 274 ms to first byte.
- Returns a proper 404 for unknown routes.
- 3 of 3 sampled routes reachable.
- No privacy policy found.
- Sets cookies / loads scripts with no consent prompt.
Who it's for
ML teams · LLM service providers · Cost-conscious AI builders · High-throughput inference users
Sources and updates
- Description
- Taken from onetriangle.ai's own website on 2026-08-25.
- Operator
- OneTriangle, as named in onetriangle.ai's terms, privacy page or footer.
- Benchmark
- Measured by Legit.Show from the live site, the way any visitor sees it — with no access to its code or accounts. Same method for every product, and no AI decides the score. Last checked 2026-11-07.
Put together from public information, without OneTriangle's involvement. If anything here is wrong, tell us and a person will check it.
Visit OneTriangle → · Alternatives to OneTriangle → · How this was measured →
