Comparison
Medusa vs exllama
Verdict
Pick Medusa if medusa enables quicker language model inference with parallel decoding strategies; pick exllama if exLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards.
Markdown twin · Medusa alternatives · exllama alternatives
GraphCanon updated Sep 20, 2026
13views this month
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Trust & integrity
| Signal | Medusa | exllama |
|---|---|---|
| Maintenance | Dormant (816d since push) As of Sep 19, 2026 · github_public_v1 | Dormant (1072d since push) As of Sep 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 19, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 7, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Medusa
- Framework for accelerating LLM generation using multiple decoding heads
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
Stars
- Medusa
- 2.8k
- exllama
- 2.9k
Forks
- Medusa
- 205
- exllama
- 220
Open issues
- Medusa
- 57
- exllama
- 65
Language
- Medusa
- Jupyter Notebook
- exllama
- Python
Adopt for
- Medusa
- Medusa enables quicker language model inference with parallel decoding strategies.
- exllama
- ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards.
Persona
- Medusa
- -
- exllama
- -
Runtime
- Medusa
- -
- exllama
- -
License
- Medusa
- Apache-2.0
- exllama
- MIT
Last pushed
- Medusa
- Jun 25, 2024
- exllama
- Sep 30, 2023
Categories
- Medusa
- Inference & Serving
- exllama
- Inference & Serving, LLM Frameworks
Trust and health
Days since push
- Medusa
- 816d
- exllama
- 1072d
Open issues (now)
- Medusa
- 57
- exllama
- 65
Stars delta
- Medusa
- +15 (30d)
- exllama
- +6 (30d)
Owner type
- Medusa
- Organization
- exllama
- User
OSV dependency advisories
- Medusa
- No lockfile (source not queried)
- exllama
- Published findings
Full report
- Medusa
- Trust report
- exllama
- Trust report
Choose Medusa if…
- Medusa is primarily Jupyter Notebook; exllama is Python.
- License: Medusa is Apache-2.0, exllama is MIT.
- Tags unique to Medusa: acceleration, decoding, inference, llm.
- When you need to accelerate inference times for large language models without compromising on output quality.
When NOT to use Medusa
- If your model does not benefit from parallelized decoding, such as when the model architecture inherently limits parallel execution efficiency.
- In scenarios where the computational resources required for multiple decoding heads exceed what is available or cost-effective within your infrastructure.
Choose exllama if…
- exllama is primarily Python; Medusa is Jupyter Notebook.
- License: exllama is MIT, Medusa is Apache-2.0.
- Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu.
- Also covers LLM Frameworks.
- exllama ships Docker support for self-hosted deployment.
- - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
When NOT to use exllama
- - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better.
- - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts).
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (FasterDecoding/Medusa) · observed Sep 20, 2026
- GitHub forks (FasterDecoding/Medusa) · observed Sep 20, 2026
- Last push (FasterDecoding/Medusa) · observed Jun 25, 2024
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (turboderp/exllama) · observed Sep 20, 2026
- GitHub forks (turboderp/exllama) · observed Sep 20, 2026
- Last push (turboderp/exllama) · observed Sep 30, 2023
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Medusa 2.8k · exllama 2.9k (synced Sep 20, 2026).
Common questions
- What is the difference between Medusa and exllama?
- Medusa: Framework for accelerating LLM generation using multiple decoding heads. exllama: Memory-efficient rewrite of HF transformers for Llama with quantized weights. See the comparison table for live GitHub stats and shared categories.
- When should I choose Medusa over exllama?
- Choose Medusa over exllama when Medusa is primarily Jupyter Notebook; exllama is Python; License: Medusa is Apache-2.0, exllama is MIT; Tags unique to Medusa: acceleration, decoding, inference, llm; When you need to accelerate inference times for large language models without compromising on output quality.
- When should I choose exllama over Medusa?
- Choose exllama over Medusa when exllama is primarily Python; Medusa is Jupyter Notebook; License: exllama is MIT, Medusa is Apache-2.0; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers LLM Frameworks; exllama ships Docker support for self-hosted deployment; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
- When should I avoid Medusa?
- If your model does not benefit from parallelized decoding, such as when the model architecture inherently limits parallel execution efficiency. In scenarios where the computational resources required for multiple decoding heads exceed what is available or cost-effective within your infrastructure.
- When should I avoid exllama?
- - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts).
- Is Medusa or exllama more popular on GitHub?
- exllama has more GitHub stars (2,943 vs 2,773). Stars measure visibility, not whether either tool fits your constraints.
- Are Medusa and exllama open source?
- Yes - both are open-source projects on GitHub (Medusa: Apache-2.0, exllama: MIT).
- Where can I find alternatives to Medusa or exllama?
- GraphCanon lists graph-backed alternatives at Medusa alternatives and exllama alternatives (Medusa markdown twin, exllama markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, Medusa or exllama?
- Medusa: Dormant. exllama: 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 Medusa and exllama?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Medusa trust report; exllama trust report.