# solvers.WeightStreamingModel { #mlsysim.solvers.WeightStreamingModel }

```python
solvers.WeightStreamingModel()
```

Analyzes Wafer-Scale inference (e.g., Cerebras CS-3) using Weight Streaming.

Instead of holding weights in HBM and streaming activations (the GPU Memory Wall),
this architecture holds massive activation batches on-wafer (SRAM) and streams
the model weights from external MemoryX nodes.

The bottleneck shifts from Memory Bandwidth to Injection Interconnect Bandwidth.

Literature Source:
1. Lie et al. (2022), "Cerebras Architecture Deep Dive: First Look Inside
   the Hardware/Software Co-Design for Deep Learning."

## Methods

| Name | Description |
| --- | --- |
| [solve](#mlsysim.solvers.WeightStreamingModel.solve) | Simulates Weight Streaming throughput and SRAM feasibility. |

### solve { #mlsysim.solvers.WeightStreamingModel.solve }

```python
solvers.WeightStreamingModel.solve(
    model,
    hardware,
    seq_len,
    batch_size=1,
    precision='fp16',
    efficiency=0.5,
    phase='decode',
)
```

Simulates Weight Streaming throughput and SRAM feasibility.

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

| Name   | Type   | Description                                                                                                                                                                                                   | Default    |
|--------|--------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|
| phase  | str    | Inference phase: 'prefill' or 'decode' (default 'decode'). - prefill: processes all S tokens in parallel (compute-heavy, O(S^2) attention) - decode: processes one token at a time per request (memory-bound) | `'decode'` |