Griffin
Definition
Griffin is a language model architecture introduced in 2024 that combines gated linear recurrence with local attention.
The architecture combines recurrent components for efficient sequence processing with local attention. It was introduced as a research approach for more efficient long sequences.
Simply explained
Like a model that remembers much continuously and only looks closely within a limited window.
Why it matters in practice
Relevant as an example of transformer alternatives and more efficient sequence architectures; not a general standard for enterprise AI.
Example from business
A research team compares Griffin with transformer and state-space architectures regarding latency, throughput, and model quality.
Limits and pitfalls
- Transferring research results unverified to own workloads; confusing Griffin with the product of the same name or other terms; interpreting benchmark values as general superiority.
Origin & history
De et al. published Griffin in 2024 together with Hawk. Griffin combines gated linear recurrences with local attention.
Use in mid-sized companies
- Possible application in medium-sized businesses mainly indirectly: technical evaluation of new model architectures for specialized on-premises or inference requirements.
Frequently asked questions
Is Griffin a general AI standard?
No, it is a specific research architecture. Does it fundamentally replace Transformers? No, suitability and available models depend on the use case.
Sources
- Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models · De et al. (2024), Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Sources checked on 10.10.2026.
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