# Laguna-S-2.1 Memory Requirements in MTPLX: Complete Hardware Guide

> Discover Laguna-S-2.1 memory requirements for MTPLX. Learn the exact RAM needed for model weights, runtime, KV-cache, and system reserve to ensure smooth operation.

- Repository: [Youssof Altoukhi/MTPLX](https://github.com/youssofal/MTPLX)
- Tags: hardware-guide
- Published: 2026-09-13

---

**Running Laguna-S-2.1 AR in MTPLX requires approximately 90.6 GiB of unified system memory**, comprising 64.12 GiB for 4-bit model weights, 8 GiB runtime headroom, an additional 2.4 GiB for KV-cache at default 32k context, and a mandatory 16 GiB system reserve.

The `youssofal/MTPLX` repository enforces these requirements through a strict pre-flight validation system that executes before loading the autoregressive checkpoint. This prevents runtime out-of-memory failures by calculating exact resident memory needs for model weights, rotating KV-cache, and per-token allocation overhead.

## Understanding the Laguna-S-2.1 Memory Footprint

MTPLX calculates memory requirements deterministically using constants defined in [`mtplx/models/laguna_config.py`](https://github.com/youssofal/MTPLX/blob/main/mtplx/models/laguna_config.py). The total splits into static and dynamic components based on the requested context length.

### Model Weights and Fixed Overhead

The base memory consumption begins with the quantized weights and fixed runtime buffers. According to [`laguna_config.py`](https://github.com/youssofal/MTPLX/blob/main/laguna_config.py), the Laguna-S-2.1 4-bit checkpoint allocates:

- **`LAGUNA_S_2_1_WEIGHT_BYTES`**: 64,122,027,323 bytes (≈ 64.12 GiB) for the frozen parameters
- **Fixed headroom**: 8 GiB reserved for activation spikes and temporary tensors
- **`LAGUNA_S_2_1_ROTATING_KV_BYTES`**: 75,497,472 bytes (≈ 0.07 GiB) for the rotating KV-cache buffer

These static allocations sum to approximately 72.19 GiB before accounting for variable context length.

### Context-Dependent KV Cache Allocation

The autoregressive model allocates additional memory proportional to the sequence length. The calculation uses:

- **`LAGUNA_S_2_1_FULL_KV_BYTES_PER_TOKEN`**: 49,152 bytes (48 KiB) per token
- **`LAGUNA_S_2_1_DEFAULT_CONTEXT`**: 32,768 tokens

At the default 32k context, the KV-cache requires approximately 1.61 GiB. The configuration exposes this through the `laguna_s_2_1_required_resident_bytes()` function, which computes:

```python
def laguna_s_2_1_required_resident_bytes(context_tokens: int) -> int:
    return (
        LAGUNA_S_2_1_WEIGHT_BYTES
        + 8 * 1024**3                                 # 8 GiB extra headroom

        + LAGUNA_S_2_1_ROTATING_KV_BYTES
        + max(1, int(context_tokens)) * LAGUNA_S_2_1_FULL_KV_BYTES_PER_TOKEN
    )

```

For the default configuration, this yields `LAGUNA_S_2_1_MIN_RESIDENT_BYTES` ≈ **74.4 GiB**.

## The MTPLX Pre-Flight Memory Check

Before initializing the model, MTPLX executes `_preflight_laguna_system_memory` within [`mtplx/runtime.py`](https://github.com/youssofal/MTPLX/blob/main/mtplx/runtime.py). This function aggregates the resident memory requirement with a non-negotiable system reserve:

```python

# mtplx/runtime.py

system_reserve = 16 * 1024**3      # 16 GiB

required = LAGUNA_S_2_1_MIN_RESIDENT_BYTES + system_reserve

```

If the host's unified memory falls below the **90.6 GiB** threshold, the runtime raises a `RuntimeError` (surfaced through [`mtplx/server/openai.py`](https://github.com/youssofal/MTPLX/blob/main/mtplx/server/openai.py)) with the message:

> “Laguna‑S‑2.1 requires at least 90.6 GiB unified memory for weights, runtime headroom, and the system reserve.”

## Code Examples for Memory Management

### Loading with Exception Handling

When initializing the runtime, wrap the loader to catch insufficient memory conditions before they crash the process:

```python
from mtplx import MTPLXRuntime, load_model
import pathlib

model_dir = pathlib.Path("/path/to/laguna-s-2.1")
try:
    runtime = MTPLXRuntime.from_filesystem(
        model_path=model_dir,
        mtp_enabled=False,                     # AR mode does not need MTP

    )
except RuntimeError as e:
    print("Cannot start Laguna‑S‑2.1 AR:", e)

```

The `RuntimeError` originates directly from `_preflight_laguna_system_memory` when your hardware cannot satisfy the calculated requirement.

### Querying Requirements Programmatically

Inspect the constants directly to determine hardware compatibility before attempting allocation:

```python
from mtplx.models.laguna_config import LAGUNA_S_2_1_MIN_RESIDENT_BYTES

min_resident_gb = LAGUNA_S_2_1_MIN_RESIDENT_BYTES / (1024**3)
total_required_gb = (LAGUNA_S_2_1_MIN_RESIDENT_BYTES + 16 * 1024**3) / (1024**3)

print(f"Minimum resident memory: {min_resident_gb:.1f} GiB")
print(f"Full requirement (incl. 16 GiB reserve): {total_required_gb:.1f} GiB")

```

This outputs the 74.4 GiB resident floor and 90.6 GiB total requirement without initializing the model.

### Validating System Memory Before Launch

Use `psutil` to compare available RAM against MTPLX requirements programmatically:

```python
import psutil
from mtplx.models.laguna_config import LAGUNA_S_2_1_MIN_RESIDENT_BYTES

total_mem_gb = psutil.virtual_memory().total / (1024**3)
required_gb = (LAGUNA_S_2_1_MIN_RESIDENT_BYTES + 16 * 1024**3) / (1024**3)

print(f"System memory detected: {total_mem_gb:.1f} GiB")
print(f"Memory needed for Laguna‑S‑2.1 AR: {required_gb:.1f} GiB")

```

Executing this validation prevents unnecessary runtime exceptions by ensuring your environment meets the 90.6 GiB threshold defined in the MTPLX source.

## Summary

- **Laguna-S-2.1** requires **74.4 GiB** of minimum resident memory for weights, 8 GiB headroom, and default context KV-cache.
- MTPLX adds a **16 GiB** system reserve, raising the total requirement to **90.6 GiB** of unified RAM.
- The pre-flight check lives in [`mtplx/runtime.py`](https://github.com/youssofal/MTPLX/blob/main/mtplx/runtime.py) → `_preflight_laguna_system_memory` and enforces this threshold before model loading.
- Memory scales linearly with context length at **48 KiB per token** beyond the base allocation.
- Insufficient memory triggers a descriptive `RuntimeError` referencing the 90.6 GiB requirement.

## Frequently Asked Questions

### How much RAM is strictly necessary to run Laguna-S-2.1 in MTPLX?

You need **approximately 90.6 GiB** of unified system memory to pass the initialization check. This breaks down into 64.12 GiB for 4-bit weights, 8 GiB runtime headroom, roughly 2.4 GiB for KV-cache at 32k context (including rotating buffers), and a mandatory 16 GiB system reserve hardcoded in [`mtplx/runtime.py`](https://github.com/youssofal/MTPLX/blob/main/mtplx/runtime.py).

### Can I run Laguna-S-2.1 with less than 90.6 GiB by reducing context length?

While the KV-cache component scales down at **48 KiB per token**, the validation in `_preflight_laguna_system_memory` currently enforces the full 90.6 GiB requirement regardless of your requested context window. The 74.4 GiB base plus 16 GiB reserve represents the static floor for all Laguna-S-2.1 instances.

### What error message appears if my system lacks sufficient memory?

MTPLX raises a `RuntimeError` with the exact text: *“Laguna‑S‑2.1 requires at least 90.6 GiB unified memory for weights, runtime headroom, and the system reserve.”* This originates from [`mtplx/runtime.py`](https://github.com/youssofal/MTPLX/blob/main/mtplx/runtime.py) and propagates through [`mtplx/server/openai.py`](https://github.com/youssofal/MTPLX/blob/main/mtplx/server/openai.py) to the user interface.

### Where are the memory constants defined in the MTPLX source?

All size calculations reference [`mtplx/models/laguna_config.py`](https://github.com/youssofal/MTPLX/blob/main/mtplx/models/laguna_config.py), which defines `LAGUNA_S_2_1_WEIGHT_BYTES`, `LAGUNA_S_2_1_FULL_KV_BYTES_PER_TOKEN`, and the `laguna_s_2_1_required_resident_bytes()` helper. The enforcement logic resides in [`mtplx/runtime.py`](https://github.com/youssofal/MTPLX/blob/main/mtplx/runtime.py) within the `_preflight_laguna_system_memory` function.