Sonar
Dphn AISonar is an inference engine for Hugging Face-compatible language and multimodal models, providing continuous batching, paged KV-cache management, optimized kernels, quantization, speculative decoding and distributed serving. It is based on vLLM.
Openness
5 medium confidence- license
- AGPL-3.0(OSI)
- source
- public(the published repository is the engine)
- core features withheld
- no — no enterprise path in the repository root and no paid build of the engine in the README
The LICENSE body is the unmodified AGPL-3.0 text. The repository is public, active and builds the engine itself, with no enterprise or paid tier in the root tree or README.
- https://api.github.com/repos/dphnAI/sonar recorded 2026-08-31
Repo metadata for dphnAI/sonar - archived false, 1,844 stars.
- https://raw.githubusercontent.com/dphnAI/sonar/main/LICENSE recorded 2026-10-07
LICENSE body, read in full: the stock GNU AGPL-3.0 text, with only template placeholders in its appendix and no appended condition.
- https://raw.githubusercontent.com/dphnAI/sonar/main/README.md recorded 2026-10-07
README, read for a paid tier, an enterprise edition or a licence-gated build beside the published source, and for whether any component is stated to be private.
Adoption
2 low confidenceNo package artifact is declared for Sonar, so GitHub stars are the only adoption signal available. The count should be read as a floor rather than a full measure of use.
- https://api.github.com/repos/dphnAI/sonar recorded 2026-08-31
Repo metadata - stargazers_count = 1,844 - for dphnAI/sonar.
Capability
4 medium confidenceSonar sits one tier below vLLM rather than level with it. Its feature list closely matches vLLM's own, but Sonar is a fork of it, so the capability is inherited rather than independently established, and SGLang holds vLLM's tier on a serving record of its own.
- https://raw.githubusercontent.com/dphnAI/sonar/main/README.md recorded 2026-10-07
README, read for the engine's feature set: Continuous batching, paged KV-cache management, optimized kernels, quantization, speculative decoding and distributed serving over Hugging Face-compatible models.
Verified 2026-09-27