--- title: "dstack vs AI-Infra-from-Zero-to-Hero" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dstack-tee-dstack-vs-huaizhengzhang-ai-infra-from-zero-to-hero" tools: ["dstack-tee-dstack", "huaizhengzhang-ai-infra-from-zero-to-hero"] --- # dstack vs AI-Infra-from-Zero-to-Hero *GraphCanon updated Aug 17, 2026* ## Verdict Pick dstack if dstack offers infrastructure for running AI workloads in Trusted Execution Environments ensuring privacy and security; pick AI-Infra-from-Zero-to-Hero if a curated resource list for AI system design focusing on large language models and various system aspects. [dstack](https://dstack.org) reports 519 GitHub stars, 91 forks, and 178 open issues, last pushed Jul 31, 2026. [AI-Infra-from-Zero-to-Hero](https://huaizheng.xyz/) has 4.3k stars, 409 forks, and 14 open issues, last pushed Jul 25, 2025. Figures are from public GitHub metadata via [dstack's repository](https://github.com/Dstack-TEE/dstack) and [AI-Infra-from-Zero-to-Hero's repository](https://github.com/HuaizhengZhang/AI-Infra-from-Zero-to-Hero). | | [dstack](/tools/dstack-tee-dstack.md) | [AI-Infra-from-Zero-to-Hero](/tools/huaizhengzhang-ai-infra-from-zero-to-hero.md) | | --- | --- | --- | | Tagline | Open framework for confidential AI | Awesome System for Machine Learning and LLM Infra | | Stars | 519 | 4,285 | | Forks | 91 | 409 | | Open issues | 178 | 14 | | Language | Rust | - | | Adopt for | Dstack offers infrastructure for running AI workloads in Trusted Execution Environments ensuring privacy and security. | A curated resource list for AI system design focusing on large language models and various system aspects. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Developer Tools | Developer Tools, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dstack](/tools/dstack-tee-dstack.md) | [AI-Infra-from-Zero-to-Hero](/tools/huaizhengzhang-ai-infra-from-zero-to-hero.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 388d | | Open issues (now) | 178 | 14 | | Stars delta | Unknown | +87 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/dstack-tee-dstack/trust.md) | [trust report](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/trust.md) | ## Decision facts: dstack - **Adopt for:** Dstack offers infrastructure for running AI workloads in Trusted Execution Environments ensuring privacy and security. ## Decision facts: AI-Infra-from-Zero-to-Hero - **Adopt for:** A curated resource list for AI system design focusing on large language models and various system aspects. ## Choose when ### Choose dstack if… - License: dstack is Apache-2.0, AI-Infra-from-Zero-to-Hero is MIT. - Tags unique to dstack: confidential-ai, intel-tdx, private-ai, safe-ai. - When you need to run confidential AI computations that require Intel TDX or NVIDIA GPUs to ensure data and code are protected from potential threats at runtime ### Choose AI-Infra-from-Zero-to-Hero if… - License: AI-Infra-from-Zero-to-Hero is MIT, dstack is Apache-2.0. - Tags unique to AI-Infra-from-Zero-to-Hero: ai-infra, genai, large language models, llmsys. - Also covers Inference & Serving, LLM Frameworks, Model Training. - When you are aiming to understand the foundational research papers, industry practices, video tutorials specific to ML systems and LLM infrastructures without requiring implementation details. ## When NOT to use dstack - Avoid if your AI workloads do not benefit from confidential computing features as the overhead of using Intel TDX may not provide any advantage and can add complexity - Not suitable for hardware setups that do not support Intel TDX or AMD SEV-SNP, limiting flexibility compared to broader hardware support offered by competitors ## When NOT to use AI-Infra-from-Zero-to-Hero - If you need step-by-step implementations for AI infrastructure setup as the repository focuses on resources rather than detailed technical instructions. - Avoid if seeking guidance specifically for real-time system deployment and tuning, since it does not cover operational tactics in depth. ## Common questions ### What is the difference between dstack and AI-Infra-from-Zero-to-Hero? dstack: Open framework for confidential AI. AI-Infra-from-Zero-to-Hero: Awesome System for Machine Learning and LLM Infra. See the comparison table for live GitHub stats and shared categories. ### When should I choose dstack over AI-Infra-from-Zero-to-Hero? Choose dstack over AI-Infra-from-Zero-to-Hero when License: dstack is Apache-2.0, AI-Infra-from-Zero-to-Hero is MIT; Tags unique to dstack: confidential-ai, intel-tdx, private-ai, safe-ai; When you need to run confidential AI computations that require Intel TDX or NVIDIA GPUs to ensure data and code are protected from potential threats at runtime. ### When should I choose AI-Infra-from-Zero-to-Hero over dstack? Choose AI-Infra-from-Zero-to-Hero over dstack when License: AI-Infra-from-Zero-to-Hero is MIT, dstack is Apache-2.0; Tags unique to AI-Infra-from-Zero-to-Hero: ai-infra, genai, large language models, llmsys; Also covers Inference & Serving, LLM Frameworks, Model Training; When you are aiming to understand the foundational research papers, industry practices, video tutorials specific to ML systems and LLM infrastructures without requiring implementation details. ### When should I avoid dstack? Avoid if your AI workloads do not benefit from confidential computing features as the overhead of using Intel TDX may not provide any advantage and can add complexity Not suitable for hardware setups that do not support Intel TDX or AMD SEV-SNP, limiting flexibility compared to broader hardware support offered by competitors ### When should I avoid AI-Infra-from-Zero-to-Hero? If you need step-by-step implementations for AI infrastructure setup as the repository focuses on resources rather than detailed technical instructions. Avoid if seeking guidance specifically for real-time system deployment and tuning, since it does not cover operational tactics in depth. ### Is dstack or AI-Infra-from-Zero-to-Hero more popular on GitHub? AI-Infra-from-Zero-to-Hero has more GitHub stars (4,285 vs 519). Stars measure visibility, not whether either tool fits your constraints. ### Are dstack and AI-Infra-from-Zero-to-Hero open source? Yes - both are open-source projects on GitHub (dstack: Apache-2.0, AI-Infra-from-Zero-to-Hero: MIT). ### Where can I find alternatives to dstack or AI-Infra-from-Zero-to-Hero? GraphCanon lists graph-backed alternatives at [dstack alternatives](/tools/dstack-tee-dstack/alternatives) and [AI-Infra-from-Zero-to-Hero alternatives](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/alternatives) ([dstack markdown twin](/tools/dstack-tee-dstack/alternatives.md), [AI-Infra-from-Zero-to-Hero markdown twin](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/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/dstack-tee-dstack-vs-huaizhengzhang-ai-infra-from-zero-to-hero.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dstack or AI-Infra-from-Zero-to-Hero? dstack: Very active. AI-Infra-from-Zero-to-Hero: Dormant. 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 dstack and AI-Infra-from-Zero-to-Hero? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dstack trust report](/tools/dstack-tee-dstack/trust); [AI-Infra-from-Zero-to-Hero trust report](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dstack-tee-dstack`](/api/graphcanon/graph?tool=dstack-tee-dstack) - 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/_