--- title: "omlx vs SiliconScope" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jundot-omlx-vs-kennss-siliconscope" tools: ["jundot-omlx", "kennss-siliconscope"] --- # omlx vs SiliconScope *GraphCanon updated Sep 20, 2026* ## Verdict Pick omlx if omlx is an LLM inference server tailored for Apple Silicon that emphasizes continuous batching and SSD caching capabilities, accessible through macOS menu bar control or Homebrew installation; pick SiliconScope if siliconScope is a sudoless system monitor for Apple Silicon, featuring detailed GUI tracking of ANE/Media Engine usage and memory bandwidth in SwiftUI applications. [omlx](https://omlx.ai) reports 22k GitHub stars, 1.9k forks, and 1.4k open issues, last pushed Sep 20, 2026. [SiliconScope](https://siliconscope.calidalab.ai/) has 955 stars, 62 forks, and 9 open issues, last pushed Sep 14, 2026. Figures are from public GitHub metadata via [omlx's repository](https://github.com/jundot/omlx) and [SiliconScope's repository](https://github.com/kennss/SiliconScope). | | [omlx](/tools/jundot-omlx.md) | [SiliconScope](/tools/kennss-siliconscope.md) | | --- | --- | --- | | Tagline | LLM inference server with continuous batching and SSD caching for Apple Silicon | Sudoless Apple Silicon system monitor with ANE/Media Engine/memory-bandwidth tracking | | Stars | 21,934 | 955 | | Forks | 1,899 | 62 | | Open issues | 1,407 | 9 | | Language | Python | Swift | | Adopt for | omlx is an LLM inference server tailored for Apple Silicon that emphasizes continuous batching and SSD caching capabilities, accessible through macOS menu bar control or Homebrew installation. | SiliconScope is a sudoless system monitor for Apple Silicon, featuring detailed GUI tracking of ANE/Media Engine usage and memory bandwidth in SwiftUI applications. | | Persona | - | - | | Runtime | - | - | | License | omlx is available under the Apache License, Version 2.0 (Apache-2.0), a permissive free software license. | SiliconScope is distributed under the MIT License, allowing for free use, modification, and distribution with full acknowledgment of origin. | | Categories | Inference & Serving | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [omlx](/tools/jundot-omlx.md) | [SiliconScope](/tools/kennss-siliconscope.md) | | --- | --- | --- | | Days since push | 0d | 5d | | Open issues (now) | 1.4k | 9 | | Stars delta | +3.3k (30d) | +127 (30d) | | Open issues delta | +552 (30d) | +8 (30d) | | Full report | [trust report](/tools/jundot-omlx/trust.md) | [trust report](/tools/kennss-siliconscope/trust.md) | ## Decision facts: omlx - **Adopt for:** omlx is an LLM inference server tailored for Apple Silicon that emphasizes continuous batching and SSD caching capabilities, accessible through macOS menu bar control or Homebrew installation. - **License detail:** omlx is available under the Apache License, Version 2.0 (Apache-2.0), a permissive free software license. ## Decision facts: SiliconScope - **Pricing:** freemium - While the core monitoring functionality comes at no cost due to its open-source nature, additional features or support may require a premium purchase. - **Requirements:** Min 4 GB RAM - **Adopt for:** SiliconScope is a sudoless system monitor for Apple Silicon, featuring detailed GUI tracking of ANE/Media Engine usage and memory bandwidth in SwiftUI applications. - **License detail:** SiliconScope is distributed under the MIT License, allowing for free use, modification, and distribution with full acknowledgment of origin. ## Choose when ### Choose omlx if… - omlx is primarily Python; SiliconScope is Swift. - License: omlx is Apache-2.0, SiliconScope is MIT. - Tags unique to omlx: inference-server. - Also covers Inference & Serving. - If your primary computing environment is based on Apple Silicon devices, omlx offers optimized performance for running large language model inferences. ### Choose SiliconScope if… - SiliconScope is primarily Swift; omlx is Python. - License: SiliconScope is MIT, omlx is Apache-2.0. - Pricing: While the core monitoring functionality comes at no cost due to its open-source nature, additional features or support may require a premium purchase.. - Requirements: Min 4 GB RAM. - Tags unique to SiliconScope: gpu, llama-cpp, lm-studio, local-llm. - Also covers Evaluation & Observability, Model Training. - When you are working on Swift-based projects targeting Apple Silicon devices and need precise monitoring of the Neural Engine and Media Engine performance ## When NOT to use omlx - If your development infrastructure relies on non-Apple Silicon hardware, omlx's specific optimizations will not be as beneficial. - Teams that require cross-platform compatibility or run servers predominantly on non-macOS operating systems should consider alternatives with broader support. - For environments where direct control through the menu bar is not practical or desired. ## When NOT to use SiliconScope - If your development environment is not centered around macOS or other Apple Silicon-based systems, as this tool lacks cross-platform compatibility - For general-purpose benchmarking that does not involve Apple-specific subsystems like ANE and the Media Engine, as its focus limits broader hardware analysis ## Common questions ### What is the difference between omlx and SiliconScope? omlx: LLM inference server with continuous batching and SSD caching for Apple Silicon. SiliconScope: Sudoless Apple Silicon system monitor with ANE/Media Engine/memory-bandwidth tracking. See the comparison table for live GitHub stats and shared categories. ### When should I choose omlx over SiliconScope? Choose omlx over SiliconScope when omlx is primarily Python; SiliconScope is Swift; License: omlx is Apache-2.0, SiliconScope is MIT; Tags unique to omlx: inference-server; Also covers Inference & Serving; If your primary computing environment is based on Apple Silicon devices, omlx offers optimized performance for running large language model inferences. ### When should I choose SiliconScope over omlx? Choose SiliconScope over omlx when SiliconScope is primarily Swift; omlx is Python; License: SiliconScope is MIT, omlx is Apache-2.0; Pricing: While the core monitoring functionality comes at no cost due to its open-source nature, additional features or support may require a premium purchase.; Requirements: Min 4 GB RAM; Tags unique to SiliconScope: gpu, llama-cpp, lm-studio, local-llm; Also covers Evaluation & Observability, Model Training; When you are working on Swift-based projects targeting Apple Silicon devices and need precise monitoring of the Neural Engine and Media Engine performance. ### When should I avoid omlx? If your development infrastructure relies on non-Apple Silicon hardware, omlx's specific optimizations will not be as beneficial. Teams that require cross-platform compatibility or run servers predominantly on non-macOS operating systems should consider alternatives with broader support. For environments where direct control through the menu bar is not practical or desired. ### When should I avoid SiliconScope? If your development environment is not centered around macOS or other Apple Silicon-based systems, as this tool lacks cross-platform compatibility For general-purpose benchmarking that does not involve Apple-specific subsystems like ANE and the Media Engine, as its focus limits broader hardware analysis ### Is omlx or SiliconScope more popular on GitHub? omlx has more GitHub stars (21,934 vs 955). Stars measure visibility, not whether either tool fits your constraints. ### Are omlx and SiliconScope open source? Yes - both are open-source projects on GitHub (omlx: Apache-2.0, SiliconScope: MIT). ### Where can I find alternatives to omlx or SiliconScope? GraphCanon lists graph-backed alternatives at [omlx alternatives](/tools/jundot-omlx/alternatives) and [SiliconScope alternatives](/tools/kennss-siliconscope/alternatives) ([omlx markdown twin](/tools/jundot-omlx/alternatives.md), [SiliconScope markdown twin](/tools/kennss-siliconscope/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/jundot-omlx-vs-kennss-siliconscope.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, omlx or SiliconScope? omlx: Very active. SiliconScope: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for omlx and SiliconScope? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [omlx trust report](/tools/jundot-omlx/trust); [SiliconScope trust report](/tools/kennss-siliconscope/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jundot-omlx`](/api/graphcanon/graph?tool=jundot-omlx) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_