--- title: "paddler vs vllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/intentee-paddler-vs-vllm-project-vllm" tools: ["intentee-paddler", "vllm-project-vllm"] --- # paddler vs vllm Neutral, constraint-first comparison with live GitHub stats. | | [paddler](/tools/intentee-paddler.md) | [vllm](/tools/vllm-project-vllm.md) | | --- | --- | --- | | Tagline | Open-source LLM load balancer and serving platform | Easy, fast, and cheap LLM serving for everyone | | Stars | 1,627 | 85,665 | | Forks | 89 | 19,107 | | Open issues | 25 | 5,589 | | Language | Rust | Python | | Adopt for | Paddler is a self-hosting platform built in Rust for managing inference and deployment of Language and Vision Models (LLMs/VLMs) on private infrastructure. It offers features like dynamic agent addition, request handling | vLLM is a high-throughput, memory-efficient inference and serving engine for Large Language Models (LLMs). It supports a wide range of models via Hugging Face integration and implements advanced techniques like Paged-AR/ | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [paddler](/tools/intentee-paddler.md) | [vllm](/tools/vllm-project-vllm.md) | | --- | --- | --- | | Days since push | 1d | 0d | | Open issues (now) | 25 | 5.6k | | Security scan | Not scanned | No lockfile | | Full report | [trust report](/tools/intentee-paddler/trust.md) | [trust report](/tools/vllm-project-vllm/trust.md) | **Typed relationship:** paddler _(integrates with)_ vllm ## Decision facts: paddler - **Requirements:** Min 8 GB RAM; Requires a Rust toolchain with MSRV of at least 1.88.0 for building from source. - **Adopt for:** Paddler is a self-hosting platform built in Rust for managing inference and deployment of Language and Vision Models (LLMs/VLMs) on private infrastructure. It offers features like dynamic agent addition, request handling ## Decision facts: vllm - **Adopt for:** vLLM is a high-throughput, memory-efficient inference and serving engine for Large Language Models (LLMs). It supports a wide range of models via Hugging Face integration and implements advanced techniques like Paged-AR/ ## Choose when ### Choose paddler if… - paddler is primarily Rust; vllm is Python. - Requirements: Min 8 GB RAM; Requires a Rust toolchain with MSRV of at least 1.88.0 for building from source.. - Graph edge: paddler is a typed integrates with of vllm - see the relationship row above. - Tags unique to paddler: llmops, ggml ecosystem, deployment, ai. - paddler ships Docker support for self-hosted deployment. - - When you need to deploy LLM and VLM models at scale with precise control over your own hardware and software environment. ### Choose vllm if… - vllm is primarily Python; paddler is Rust. - Graph edge: vllm is a typed integrates with of paddler - see the relationship row above. - Tags unique to vllm: amd, llama, deepseek, cuda. - - When you need state-of-the-art throughput with efficient attention management using **PagedAttention**. ## When NOT to use paddler - - If you prefer platforms with a larger community base or extensive third-party integrations. Paddler may not offer the depth of support for specific use cases as more established competitors might. - - For those looking for extensive pre-built model integration and automation tools, since Paddler focuses on minimalistic setup around ggml ecosystem. ## When NOT to use vllm - - For users who do not require or cannot support the hardware and software dependencies such as CUDA/HIP for optimal performance. - - If your project focuses on model training rather than inference since vLLM's primary strength lies in serving and high-throughput applications. - - When you need a tool that is highly portable to older or less common architectures, given its optimization for modern GPUs and specialized hardware might not be beneficial in those scenarios. ## Common questions ### What is the difference between paddler and vllm? paddler: Open-source LLM load balancer and serving platform. vllm: Easy, fast, and cheap LLM serving for everyone. See the comparison table for live GitHub stats and shared categories. ### When should I choose paddler over vllm? Choose paddler over vllm when paddler is primarily Rust; vllm is Python; Requirements: Min 8 GB RAM; Requires a Rust toolchain with MSRV of at least 1.88.0 for building from source.; Graph edge: paddler is a typed integrates with of vllm - see the relationship row above; Tags unique to paddler: llmops, ggml ecosystem, deployment, ai; paddler ships Docker support for self-hosted deployment; - When you need to deploy LLM and VLM models at scale with precise control over your own hardware and software environment. ### When should I choose vllm over paddler? Choose vllm over paddler when vllm is primarily Python; paddler is Rust; Graph edge: vllm is a typed integrates with of paddler - see the relationship row above; Tags unique to vllm: amd, llama, deepseek, cuda; - When you need state-of-the-art throughput with efficient attention management using **PagedAttention**. ### When should I avoid paddler? - If you prefer platforms with a larger community base or extensive third-party integrations. Paddler may not offer the depth of support for specific use cases as more established competitors might. - For those looking for extensive pre-built model integration and automation tools, since Paddler focuses on minimalistic setup around ggml ecosystem. ### When should I avoid vllm? - For users who do not require or cannot support the hardware and software dependencies such as CUDA/HIP for optimal performance. - If your project focuses on model training rather than inference since vLLM's primary strength lies in serving and high-throughput applications. - When you need a tool that is highly portable to older or less common architectures, given its optimization for modern GPUs and specialized hardware might not be beneficial in those scenarios. ### Is paddler or vllm more popular on GitHub? vllm has more GitHub stars (85,665 vs 1,627). Stars measure visibility, not whether either tool fits your constraints. ### Are paddler and vllm open source? Yes - both are open-source projects on GitHub (paddler: Apache-2.0, vllm: Apache-2.0). ### Where can I find alternatives to paddler or vllm? GraphCanon lists graph-backed alternatives at /tools/intentee-paddler/alternatives and /tools/vllm-project-vllm/alternatives (/tools/intentee-paddler/alternatives.md, /tools/vllm-project-vllm/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 /compare/intentee-paddler-vs-vllm-project-vllm.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, paddler or vllm? paddler: Very active. vllm: 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 paddler and vllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: paddler: /tools/intentee-paddler/trust; vllm: /tools/vllm-project-vllm/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=intentee-paddler`](/api/graphcanon/graph?tool=intentee-paddler) - 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/_