Principle:Ggml org Llama cpp Memory Management

Knowledge Sources	Ggml_org_Llama_cpp
Domains	Memory, Architecture
Last Updated	2026-02-15 00:00 GMT

Overview

Memory Management is the principle of abstracting and coordinating state storage across different model architecture types during inference.

Description

This principle defines the memory abstraction layer that sits above specific cache implementations (KV cache, recurrent state) and provides a unified interface for managing inference state. Different model architectures require different state management strategies: transformer models use KV caches, recurrent models (such as Mamba/RWKV) maintain hidden states, and hybrid models combine both. The memory management layer provides a common interface that the inference engine uses regardless of the underlying architecture.

Usage

Apply this principle when implementing support for new model architectures that require novel state management strategies, or when the inference engine needs to perform architecture-agnostic operations on cached state such as saving, loading, or copying sequences.

Theoretical Basis

The memory management abstraction follows the strategy pattern, where the specific memory implementation is selected based on the model architecture. Recurrent memory manages fixed-size hidden states that are updated at each step rather than growing linearly with sequence length. Hybrid memory combines KV cache and recurrent state for architectures that interleave attention and recurrent layers. The ISWA hybrid variant further specializes this for models that mix full attention, sliding window attention, and recurrent layers. This layered abstraction allows the core inference loop to remain agnostic to the specifics of state management.

Related Pages

Page Connections

Double-click a node to navigate. Hold to expand connections.

Principle

Implementation

Heuristic

Environment