# core.solver.TrainingMemoryModel { #mlsysim.core.solver.TrainingMemoryModel }

```python
core.solver.TrainingMemoryModel()
```

Decomposes per-accelerator training memory into weights, gradients, optimizer
state, activations, and communication buffers.

This model is intended for first-order training feasibility analysis. It makes
the difference between inference memory and training memory explicit without
modeling framework internals.

## Methods

| Name | Description |
| --- | --- |
| [solve](#mlsysim.core.solver.TrainingMemoryModel.solve) | Estimate per-accelerator training memory. |

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

```python
core.solver.TrainingMemoryModel.solve(
    model,
    hardware,
    batch_size,
    seq_len=2048,
    precision='fp16',
    optimizer='adam',
    activation_checkpointing='selective',
    tp_size=1,
    pp_size=1,
    dp_size=1,
    ep_size=1,
    zero_stage=0,
    gradient_accumulation_steps=1,
    trainable_fraction=1.0,
    communication_buffer_fraction=0.05,
)
```

#### Parameters

| Name | Type | Description | Default |
| --- | --- | --- | --- |
| model | TransformerWorkload | Transformer workload to train. | _required_ |
| hardware | HardwareNode | Per-rank accelerator target. | _required_ |
| batch_size | int | Global batch size. | _required_ |
| seq_len | int | Training sequence length. | `2048` |
| precision | str | Parameter/gradient precision. | `'fp16'` |
| optimizer | str | `adam`, `adamw`, `sgd`, or `none`. | `'adam'` |
| activation_checkpointing | str | `none`, `selective`, or `full`. | `'selective'` |
| tp_size, pp_size, dp_size, ep_size | int | Parallelism degrees. | `1` |
| zero_stage | int | ZeRO stage 0--3. | `0` |
| gradient_accumulation_steps | int | Steps used to derive local microbatch. | `1` |
| trainable_fraction | float | Fraction of local parameters with gradients and optimizer state. | `1.0` |
| communication_buffer_fraction | float | Gradient bucket buffer fraction. | `0.05` |

#### Returns

`TrainingMemoryResult` with total memory, available memory, feasibility,
utilization, and a component breakdown.