# core.solver.ContinuousBatchingModel { #mlsysim.core.solver.ContinuousBatchingModel }

```python
core.solver.ContinuousBatchingModel()
```

Analyzes production LLM serving with Continuous Batching and PagedAttention.

Traditional static batching suffers from severe memory fragmentation and
padding waste. This solver models 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.core.solver.ContinuousBatchingModel.solve) | Solves for continuous batching throughput and PagedAttention memory. |

### solve { #mlsysim.core.solver.ContinuousBatchingModel.solve }

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

Solves for continuous batching throughput and PagedAttention memory.

#### Parameters {.doc-section .doc-section-parameters}

| Name | Type | Description | Default |
|------|------|-------------|---------|
| model | TransformerWorkload | The LLM model architecture. | _required_ |
| hardware | HardwareNode | The target hardware for inference. | _required_ |
| seq_len | int | The total context window (prompt + generated tokens). | _required_ |
| max_batch_size | int | Maximum concurrent requests in the batch. | `1` |
| page_size | int | Tokens per KV cache page (PagedAttention granularity). | `16` |
| precision | str | Numerical format (fp16, int8, int4). | `'fp16'` |
| efficiency | float | Compute utilization efficiency (0.0 to 1.0). | `0.5` |

#### Returns {.doc-section .doc-section-returns}

| Name | Type | Description |
|------|------|-------------|
| | ContinuousBatchingResult | Throughput (tokens/s), max active requests, memory fragmentation, TTFT, ITL, and speedup vs. static batching. |