Dlama

Dlama is a term used in the field of scalable machine learning to describe a family of software architectures aimed at training and serving large language models across distributed hardware. The name is not tied to a single project but denotes approaches that separate the model, data, and orchestration layers to enable elastic scaling and efficient resource use.

Architecture in a typical dlama stack includes a model shard manager that maps parameter partitions to devices, a data pipeline that feeds micro-batches, a scheduler that coordinates training steps, and a communication layer for inter-device synchronization. It supports multiple parallelism strategies, including data parallelism, tensor or model parallelism, and pipeline parallelism, as well as techniques for memory management, offloading, and device utilization across GPUs, TPUs, or other accelerators.

Workflow during training involves partitioning the model and data across devices and using a scheduler to coordinate updates, with synchronization steps that maintain consistency across shards. For inference, the system routes requests to the appropriate model shards and merges outputs to produce a final result. Key goals include fault tolerance, elastic scaling, low latency, and efficient bandwidth and memory usage.

Development and usage of dlama concepts appear across research and industry discussions, often inspiring open-source prototypes and middleware components. Implementations tend to be designed to integrate with existing machine learning frameworks while remaining as framework-agnostic as possible, enabling experimentation with different backends and hardware configurations.

The dlama approach reflects broader efforts to improve the scalability and cost-effectiveness of large language models, particularly when moving beyond single-node or single-accelerator deployments and preparing for cluster-wide deployment environments.