Domestic GPUs Run Inference: Don't Rush to Production

Running a minimal model inference (i.e., having a trained model answer questions) on the MTT S4000/S3000 full-featured GPU is suitable for learning and small-scale validation, but it's not ready to be treated as a production solution directly. I'll walk beginners through the process from launching an instance to getting results, laying out the pros and cons first.

The MetaX Platform is Moore Threads' entry point for developers to provision machines and manage environments. It packages machines, drivers, and environments together, saving you the hassle of installing systems, configuring drivers, and finding images yourself. The S4000 specs list 48GB VRAM per card, which is friendly for models ranging from a few GB to over ten GB. The full-featured GPU can also handle rendering and video processing, making it suitable for validating miscellaneous tasks.

The software ecosystem isn't plug-and-play. People used to CUDA (Nvidia's software layer for calling GPUs) might stumble here. Error messages may not be as intuitive as on mainstream platforms, leading beginners to guess wildly. "It runs" doesn't equal "it's stable"; latency, throughput, and compatibility all need to be stress-tested by you.

Here is the path forward. Entry names might vary slightly; just look for similar options like Instance Management or Create Instance.

1. Register and log in to the MetaX Platform, find Instance Management, and click Create Instance. Select MTT S3000, and choose an image that includes MUSA and PyTorch. An image is a pre-installed system template. MUSA is Moore Threads' own software stack for programs to call the GPU, while PyTorch is the framework for writing models and inference scripts. Don't go overboard with configuration initially: 1 card, 8GB+ system RAM, and sufficient disk space. Fill in your SSH public key; if you don't have one, select password authentication. Click Create. Once the status changes from Creating to Running, you should generally be able to access it.

2. Copy the public IP and connect via terminal using ssh root@yourIP. SSH is a remote login command. On the first connection, it will ask if you trust the host; type yes. Seeing the command prompt means the machine is reachable.

3. Run the check commands provided by the platform, such as musa-smi. If you see the GPU name, VRAM, and utilization rates, it means the driver and runtime are alive. If you don't see this, stop there. Check the platform status or open a ticket first; don't rush to install things.

4. Create a working directory with mkdir -p /data/infer && cd /data/infer and place the small model weights inside. Weights are the model parameter files. Beginners shouldn't use tens-of-GB models; just grab a few-GB open-source small model. You can upload locally or download within the machine. If downloads are slow, upload locally—don't waste time waiting on the network.

5. Write a shortest possible inference script. The core is just a few lines: load the model, set the device, input a prompt, and print the result. Do not write cuda:0 for the device; follow the image instructions and use musa:0 first. A command looks something like python infer.py --model ./model --prompt "Explain GPU VRAM in one sentence" --device musa:0.

6. Wait for output. The first run might require initialization, so being slow is normal. Once you see the model respond, check musa-smi again to see if VRAM usage has changed. If so, this step is successfully completed.

Pitfalls section:

First pitfall: Running old CUDA scripts directly leads to errors saying torch.cuda is unavailable. Change the device first. Don't compile extensions yourself; use the platform's pre-installed environment.

Second pitfall: Insufficient VRAM because the model is too large, causing the process to be killed. Switch to a smaller model or reduce batch length (the number of characters fed in at once). If you need to run models over ten-something GB, then consider the S4000's 48GB VRAM.

Third pitfall: Thinking multi-card training is as simple as plugging in a USB drive. S4000 docs mention MTLink and multi-card clusters. MTLink is a high-speed inter-card channel, but for serious training of hundred-billion-parameter models, network, scheduling, storage, and framework adaptation are all essential.

Fourth pitfall: Only checking if it answers once, ignoring stability. In my tests, running successfully once versus running continuously for an hour without crashing are two different levels of difficulty.

The specs list the S4000 with 48GB VRAM per card, 768GB/s bandwidth, and support for multi-card interconnects. The numbers look good, but real-world deployment depends on the toolchain and actual workloads. It is suitable for introductory validation, small-sample inference, and feasibility prototyping for projects, but not for handling serious production traffic right away. I wrote about semiconductor revenue last week; don't rush to shout about AI implementation. This applies equally to domestic GPUs: upstream compute power being busy doesn't mean downstream delivery is mature.

After learning this, the next step is to try running the same model on CPU, S3000, and S4000 separately. CPU is the standard processor. Record first response time, peak VRAM usage, and average time over ten consecutive runs. Decide whether to move to training only after seeing the data.