The MiniMax-M3.1-Flash model is a compact large language model released by the MiniMax team under the Stratat ecosystem. It is intended for fully local inference: you can run it on a personal computer, tablet or edge device without connecting to any remote server, keeping your prompts and outputs private. Stratat provides a client that bundles the model loader and runtime so you can launch the model with a single command on standard x86 and ARM machines.
Why a lightweight local model matters
Most AI services stream your prompts to corporate datacenters. Running locally means your data never leaves your machine, and you avoid sending terabytes of text to external services. A model small enough to fit in RAM and cache lets you interact with AI on devices that have no internet connection, which is useful for privacy-sensitive workflows.
How the model is built for Stratat
The model is designed to fit within the memory constraints of consumer laptops and small edge boxes. Stratat adds a small amount of tooling and runtime glue around the base model, but the model weights themselves remain unchanged. Using the Stratat client you specify the path to the weight file, choose the target device (CPU, GPU or NPU if your hardware exposes a supported accelerator), and run a single launch command. The client also handles quantization-friendly loading, so the same file can be run in different precisions depending on the hardware you select.
| Device | Required hardware | Approximate RAM usage | Approximate time to first token |
|---|---|---|---|
| Modern laptop CPU (x86, 8+ cores) | No accelerator needed | ~4 GB for the base flash variant | Roughly 20-30 seconds per token on a single core test |
| Discrete GPU (NVIDIA, 6+ GB VRAM) | Dedicated GPU with API support | ~2.5 GB for the flash variant | Roughly 10-15 seconds per token |
| Edge NPU or ARM mobile device | On-device NPU acceleration | ~1.5 GB for the flash variant | Roughly 12-20 seconds per token |
To measure these numbers on your own machine, open a terminal and run the Stratat CLI with the model path set to the downloaded MiniMax-M3.1-Flash weights, then time a single generation request. For example, use `stratet run --model MiniMax-M3.1-Flash --device cpu <input.txt> > /dev/null` and note the wall-clock time shown after one generated token. Repeat the same command with the `--device gpu` flag and verify that the reported time matches the category in the table. You can also inspect the memory usage with `stratet info --model MiniMax-M3.1-Flash --device gpu`, which should report the amount of GPU memory consumed during inference.
What "local inference" really means here
The model is executed entirely on the hardware you specify. Nothing is sent over the network, and the Stratat client does not contact any online service when you invoke it. Your prompts stay on your local filesystem, and the output lands in your local output buffer. This property is what makes the model useful for confidential documents, coding assistance, or any workflow where data leakage is a concern.
Core definitions
Minimum viable hardware for running the flash variant locally refers to any computer with enough RAM, GPU memory, or NPU support to hold the model file and run the inference pipeline without offloading to a remote server.
How to check the model is loaded correctly
Run the following command and confirm the model identifier appears in the status line: `stratet load --model MiniMax-M3.1-Flash --device cpu`. If the model loads successfully, the client will print a short confirmation message including the model name and the device type. You can then compare the output length and style against a known sample to verify consistency.
Measuring performance yourself
The quickest reproducible measurement is a single-token generation timing suite: start the model, feed it a short prompt, stop the timer after the first generated token, and record the elapsed seconds. Repeat the test three times and take the median value. Compare it against the rough ranges listed in the table above. If your device has an NVIDIA GPU, set `CUDA_VISIBLE_DEVICES=0` before launching to ensure the correct card is used. For CPUs, prefer the highest core count available, but the timing should be similar across a single core versus multiple cores when the model is already resident in cache.
A core term explanation Local inference is the process of running a large language model on your own hardware without sending inputs or outputs to an external service.
When to pick the flash variant
Choose the flash variant if you need a small model that fits in under 8 GB of RAM and you want private AI on a laptop or small edge box. Pick a larger model only if you have more memory and a powerful GPU or NPU, but the flash size is the most portable starting point for anyone wanting to experiment with local AI in Stratat.