AI Model Downloads Too Slow? A Complete Guide to Accelerated Downloads and Local Deployment in China

When deploying AI large language models locally, most people run into two unavoidable pain points: model files are often tens of gigabytes in size, making downloads agonizingly slow; and once the download finally finishes, it's unclear how to integrate the model files into different deployment tools. Based on a hands-on demonstration from a Bilibili content creator, this article systematically walks through the complete workflow for accelerated downloading and local adaptation — so you can skip the trial and error.

Why Are Large Model Downloads So Slow?

The reason these models are called "large" comes down to file size. A quantized model file typically ranges from 6 GB to 20 GB — and that's on the smaller end. If you download directly from overseas model hosting platforms like Hugging Face, users in China often experience speeds of just a few dozen KB/s or barely over 100 KB/s. Spending several hours downloading a single model is completely normal.

The root cause is network routing and server geography. There are two approaches to solving this: first, use a model community that's optimized for users in China; second, replace direct browser downloads with a dedicated download tool. Let's cover each in detail.

Method 1: Accelerated Downloads via ModelScope

ModelScope is an open-source AI model community co-founded by Alibaba and related computing committees. Its servers are located in China, making it naturally fast and user-friendly for Chinese users.

Finding Your Target Model

Inside ModelScope's model library, you can filter by task type — for example, selecting "Text Generation" will narrow down the results to that category. The community hosts popular open-source models including DeepSeek, Zhipu GLM, and many others.

Here's a practical tip: before downloading, always check the "Community Feedback" section on the model's homepage. This is where real users share their experiences. A high volume of positive feedback is a good signal that the model is reliable and worth downloading — a low-cost way to gauge a model's usability.

Downloading a Single Model File via Command Line

ModelScope provides clear instructions on each model's page. The process has two steps:

1. Install the ModelScope tool: Copy the installation command provided on the page and run it in your terminal.
2. Download the specific file: Copy the single-file download command. Pay attention to a few key parameters — the value after model corresponds to the model repository name and must match the actual repository exactly; the filename refers to the specific model file you want (e.g., a 6.19 GB quantized version); and ./ as the download directory means the file will be saved to the current folder.

Copy the command into the terminal opened in your target folder and press Enter to start the download. Because the traffic routes through domestic infrastructure, speeds are noticeably faster — the download should complete in a reasonable amount of time.

Method 2: Speed Up Downloads with a Dedicated Download Tool

Beyond ModelScope, there's a more universal approach — replacing browser-based downloads with a dedicated download tool. This method isn't tied to any specific community and works with nearly any model download link.

Installing and Verifying the Tool

Install the download tool via the command line, then run the version check command to verify the installation. If the version number displays correctly, you're good to go.

Real-World Speed Comparison

The video demonstrates a clear side-by-side comparison: downloading a 7.27 GB model directly in Google Chrome yields speeds of just over 100 KB/s. Switching to the dedicated download tool — by plugging the model name and download URL into the command and running it in the target folder's terminal — pushes speeds up to 5–6 MB/s.

One important caveat: if you have a proxy enabled (e.g., a VPN) while using the download tool, you may encounter errors. This is not a problem with the software or the model — it's a network resource conflict between the two tools running simultaneously. Using the download tool on its own, without a proxy active, works fine.

Integrating Downloaded Models into Local Deployment Tools

Once the model file is downloaded, you need to "import" it into your deployment tool of choice before you can use it. Here's how to do that for two popular tools: LM Studio and Ollama.

Importing a Model into LM Studio

LM Studio's import process is relatively straightforward:

1. Right-click inside the folder containing the model file and open a terminal window.
2. Run the lms import command (where lms is LM Studio's CLI tool), followed by the model filename.
3. Press Enter, follow the prompts to select the path, and optionally give the model a custom name (e.g., abc).

Once complete, open LM Studio and you'll see the imported model in your library. Test it in the chat interface to confirm it loads correctly.

Importing a Model into Ollama (Using a Modelfile)

The Ollama workflow is slightly more involved and requires a configuration file:

1. Create a Modelfile: In the directory where the model is stored, create a new file named Modelfile (pay attention to capitalization and naming conventions).
2. Write the configuration: Open the file in a text editor and add the line FROM ./your-model-filename, pointing to the model file in the current directory. The path and filename must exactly match the actual file, or the import will fail.
3. Run the create command: Save the file, open a terminal, and run ollama create model-name -f Modelfile, where model-name is whatever you want to call it (e.g., qianwen) and -f specifies the Modelfile as the source.

After the command completes, open Ollama and select the newly created model in the chat interface. You can verify it loaded correctly by watching GPU VRAM usage climb in your performance monitor as the model loads.

Successful Import ≠ Good Performance: The Impact of Model Scale

The video includes an important observation worth highlighting: the content creator imported a small 5.76 GB model that imported without any issues, but the actual conversations produced errors and completely off-topic responses.

It's important to distinguish between two separate concepts here:

• Whether the import succeeded: This is a tooling and configuration issue. If you follow the steps correctly, it will work.
• Whether the model performs well: This depends on the model's scale and quality. Smaller file sizes generally mean fewer parameters, which typically means weaker performance.

So if you find the model's responses are poor after importing, don't immediately assume you made a mistake. The model may simply be too small to handle complex tasks. For better results, choose a larger model within the limits of your hardware.

Summary

Local large model deployment comes down to solving two core problems — speed and compatibility:

• Slow downloads: Prioritize ModelScope for its domestic servers and Chinese-language support, or use a dedicated download tool instead of a browser. Either approach can improve speeds by orders of magnitude.
• Import challenges: LM Studio supports one-command import via lms import; Ollama requires creating a Modelfile configuration and running ollama create.
• Poor output quality: First check whether the model itself is too small — this is usually the culprit, not a mistake in your workflow.

Once you've mastered this process, the barrier to locally deploying large language models drops significantly. For details on setting up deployment environments and integrating with applications like DeepSeek, refer to the relevant advanced tutorials.