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在每个 GPU 上运行多个模型实例

使用并发执行提高利用率

在每个 GPU 上运行多个模型实例 是 CoddyKit 上的免费 MLOps Academy 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 MLOps Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 MLOps Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

One Copy Can Stall

With a single model copy, request two must wait while request one runs. Even a fast GPU can sit idle between calls, leaving throughput on the table.

Run Several Copies

Triton can load multiple instances of the same model so several requests execute concurrently and overlap their work on the GPU.

The instance_group Block

You declare copies with an instance_group in config.pbtxt. The count field says how many instances Triton should create for that model.

instance_group {
  count: 2
  kind: KIND_GPU
}

Pick GPU or CPU

The kind field chooses the device. KIND_GPU runs instances on the GPU, while KIND_CPU runs them on the host processor instead.

Place Them on GPUs

You can pin instances to specific cards with a gpus list. This lets one model spread copies across several GPUs in the same server.

instance_group {
  count: 2
  kind: KIND_GPU
  gpus: [ 0, 1 ]
}

Why It Helps

While one instance does math, another can load inputs or copy results. This overlap hides idle gaps and lifts overall utilization.

It Pairs With Batching

Instances and dynamic batching work together. Batching fills each call, while multiple instances keep more than one call in flight at once.

Watch the Memory

Each instance holds its own copy of the weights in GPU memory. Too many copies and you run out of VRAM, so raise the count gradually.

More Is Not Always Faster

Past a point, extra instances just compete for the same compute. Throughput plateaus or drops, so the best count comes from measuring, not guessing.

Concurrency in Mind

The right instance count depends on how many requests arrive at once. Match instances to your real concurrency to avoid both stalls and waste.

A Sensible Starting Point

Two instances per GPU is a common starting point. Test with realistic load, then adjust the count up or down based on what you observe.

Quick Check

What does setting count to 2 in an instance_group do?

Recap

You learned to run several model copies via instance_group, pairing instances with batching for parallelism, while watching VRAM and tuning the count by measurement. 🙌

常见问题解答

「在每个 GPU 上运行多个模型实例」课时是免费的吗？

是的 — 「在每个 GPU 上运行多个模型实例」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 MLOps Academy 课程的其余内容，请升级到 CoddyKit PRO。 MLOps Academy 课程共包含 4 节课。

「在每个 GPU 上运行多个模型实例」这节课中我会学到什么？

使用并发执行提高利用率你通过在浏览器中直接运行的动手代码来练习 MLOps Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 MLOps Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 MLOps Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

「在每个 GPU 上运行多个模型实例」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 MLOps Academy 课中编写并运行代码吗？

能。每节 MLOps Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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