# solvers.ContinuousBatchingModel { #mlsysim.solvers.ContinuousBatchingModel }

```python
solvers.ContinuousBatchingModel()
```

Analyzes production LLM serving with Continuous Batching and PagedAttention.

Traditional static batching suffers from severe memory fragmentation and
padding waste. This model simulates the throughput improvements achieved by
iteration-level scheduling and non-contiguous KV cache allocation.

Literature Source:
1. Kwon et al. (2023), "Efficient Memory Management for Large Language Model
   Serving with PagedAttention."
2. Yu et al. (2022), "ORCA: A Distributed Serving System for
   Transformer-Based Generative Models."

## Methods

| Name | Description |
| --- | --- |
| [solve](#mlsysim.solvers.ContinuousBatchingModel.solve) | Calculates continuous batching throughput and PagedAttention memory. |

### solve { #mlsysim.solvers.ContinuousBatchingModel.solve }

```python
solvers.ContinuousBatchingModel.solve(
    model,
    hardware,
    seq_len,
    max_batch_size=1,
    page_size=16,
    precision='fp16',
    efficiency=0.5,
)
```

Calculates continuous batching throughput and PagedAttention memory.