---
title: "REST vs llama.cpp"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/fasterdecoding-rest-vs-ggml-org-llama-cpp"
tools: ["fasterdecoding-rest", "ggml-org-llama-cpp"]
---

# REST vs llama.cpp

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick REST if rEST is a retrieval-based speculative decoding tool implemented in C, designed for use cases that demand efficiency and fine-grained control over inference processes through its distinctive approach; pick llama.cpp if llama.cpp is a C++ framework for LLM inference, offering versatile installation options including package managers, Docker, and binary downloads.

[REST](https://github.com/FasterDecoding/REST) reports 220 GitHub stars, 17 forks, and 15 open issues, last pushed Mar 5, 2026. [llama.cpp](https://llama.app) has 120k stars, 20k forks, and 1.8k open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [REST's repository](https://github.com/FasterDecoding/REST) and [llama.cpp's repository](https://github.com/ggml-org/llama.cpp).

| | [REST](/tools/fasterdecoding-rest.md) | [llama.cpp](/tools/ggml-org-llama-cpp.md) |
| --- | --- | --- |
| Tagline | REST: Retrieval-Based Speculative Decoding | LLM inference in C/C++ |
| Stars | 220 | 120,002 |
| Forks | 17 | 20,446 |
| Open issues | 15 | 1,841 |
| Language | C | C++ |
| Adopt for | REST is a retrieval-based speculative decoding tool implemented in C, designed for use cases that demand efficiency and fine-grained control over inference processes through its distinctive approach. | llama.cpp is a C++ framework for LLM inference, offering versatile installation options including package managers, Docker, and binary downloads. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | MIT licensed, allowing free use and modification under certain conditions. |
| Categories | Data & Retrieval, Inference & Serving | Inference & Serving |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [REST](/tools/fasterdecoding-rest.md) | [llama.cpp](/tools/ggml-org-llama-cpp.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Very active (96%) |
| Days since push | 128d | 0d |
| Open issues (now) | 15 | 1.8k |
| Security scan | 2 low (2 low) | No criticals |
| Full report | [trust report](/tools/fasterdecoding-rest/trust.md) | [trust report](/tools/ggml-org-llama-cpp/trust.md) |

## Decision facts: REST

- **Adopt for:** REST is a retrieval-based speculative decoding tool implemented in C, designed for use cases that demand efficiency and fine-grained control over inference processes through its distinctive approach.

## Decision facts: llama.cpp

- **Hosting:** unknown - llama.cpp supports various installation methods including package managers (like brew), Docker containers for isolation, pre-built binaries for ease of deployment, and source builds for flexibility.
- **Requirements:** Installation can be done via multiple channels including package managers, Docker, and direct downloads.
- **Adopt for:** llama.cpp is a C++ framework for LLM inference, offering versatile installation options including package managers, Docker, and binary downloads.
- **License detail:** MIT licensed, allowing free use and modification under certain conditions.

## Choose when

### Choose REST if…

- REST is primarily C; llama.cpp is C++.
- License: REST is Apache-2.0, llama.cpp is MIT.
- Tags unique to REST: llm-inference, retrieval, speculative-decoding.
- Also covers Data & Retrieval.
- - When you need high performance and are willing to work with the C language for customization and optimization.

### Choose llama.cpp if…

- llama.cpp is primarily C++; REST is C.
- License: llama.cpp is MIT, REST is Apache-2.0.
- llama.cpp supports various installation methods including package managers (like brew), Docker containers for isolation, pre-built binaries for ease of deployment, and source builds for flexibility.
- Requirements: Installation can be done via multiple channels including package managers, Docker, and direct downloads..
- Tags unique to llama.cpp: c++, ggml.
- - You need high-performance inference capabilities in a lightweight environment where C++ performance benefits are critical.

## When NOT to use REST

- - Avoid if your team lacks proficiency in C programming as this may lead to an overhead in developing and maintaining the tool.
- - Not recommended for projects where flexibility with commonly used high-level languages like Python is essential, as REST primarily relies on lower-level language capabilities.

## When NOT to use llama.cpp

- - If you prefer a language other than C++, as this tool lacks support for Python or JavaScript bindings that provide higher-level abstractions.
- - When your project demands extensive runtime customization and flexibility that is more easily achieved in languages like Python with libraries such as PyTorch.

## Common questions

### What is the difference between REST and llama.cpp?

REST: REST: Retrieval-Based Speculative Decoding. llama.cpp: LLM inference in C/C++. See the comparison table for live GitHub stats and shared categories.

### When should I choose REST over llama.cpp?

Choose REST over llama.cpp when REST is primarily C; llama.cpp is C++; License: REST is Apache-2.0, llama.cpp is MIT; Tags unique to REST: llm-inference, retrieval, speculative-decoding; Also covers Data & Retrieval; - When you need high performance and are willing to work with the C language for customization and optimization.

### When should I choose llama.cpp over REST?

Choose llama.cpp over REST when llama.cpp is primarily C++; REST is C; License: llama.cpp is MIT, REST is Apache-2.0; llama.cpp supports various installation methods including package managers (like brew), Docker containers for isolation, pre-built binaries for ease of deployment, and source builds for flexibility; Requirements: Installation can be done via multiple channels including package managers, Docker, and direct downloads.; Tags unique to llama.cpp: c++, ggml; - You need high-performance inference capabilities in a lightweight environment where C++ performance benefits are critical.

### When should I avoid REST?

- Avoid if your team lacks proficiency in C programming as this may lead to an overhead in developing and maintaining the tool. - Not recommended for projects where flexibility with commonly used high-level languages like Python is essential, as REST primarily relies on lower-level language capabilities.

### When should I avoid llama.cpp?

- If you prefer a language other than C++, as this tool lacks support for Python or JavaScript bindings that provide higher-level abstractions. - When your project demands extensive runtime customization and flexibility that is more easily achieved in languages like Python with libraries such as PyTorch.

### Is REST or llama.cpp more popular on GitHub?

llama.cpp has more GitHub stars (120,002 vs 220). Stars measure visibility, not whether either tool fits your constraints.

### Are REST and llama.cpp open source?

Yes - both are open-source projects on GitHub (REST: Apache-2.0, llama.cpp: MIT).

### Where can I find alternatives to REST or llama.cpp?

GraphCanon lists graph-backed alternatives at [REST alternatives](/tools/fasterdecoding-rest/alternatives) and [llama.cpp alternatives](/tools/ggml-org-llama-cpp/alternatives) ([REST markdown twin](/tools/fasterdecoding-rest/alternatives.md), [llama.cpp markdown twin](/tools/ggml-org-llama-cpp/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/fasterdecoding-rest-vs-ggml-org-llama-cpp.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, REST or llama.cpp?

REST: Slowing. llama.cpp: 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 REST and llama.cpp?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [REST trust report](/tools/fasterdecoding-rest/trust); [llama.cpp trust report](/tools/ggml-org-llama-cpp/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=fasterdecoding-rest`](/api/graphcanon/graph?tool=fasterdecoding-rest)
- 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/_