Let’s talk about something cool - distributed batch inference for LLMs. It sounds technical, but it’s actually a neat approach for teams working with large language models. Batch inference can significantly reduce your inference costs(2-3x cheaper vs traditional inference, more on that below).

As a bonus I am going to show you how to spin up your own inference for DeepSeek r1 658B at scale with no rate limits. Link to the notebook with the working code is at the bottom of the post.

What is distributed batch inference for LLMs?

Imagine you’ve got thousands (or millions) of prompts to run through an LLM—say, product descriptions, customer reviews, or support tickets. Batch inference (aka bulk inference or offline inference) lets you process all of them at once, in bulk, instead of doing them one-by-one like in real-time applications (think chatbots). It’s like cooking a big pot of pasta instead of making one noodle at a time—way more efficient.

Now, take that and distribute it across multiple nodes of GPUs, and boom—you’ve got distributed batch inference.

When should you consider distributed batch inference for LLMs?

If you don't need instant responses (higher latency is ok for you) and you’ve got tons of data to process, distributed batch inference is the optimal choice. It saves time, money, and gives you the scale you need to handle big projects without breaking the bank. Here's a non-exhaustive list of recommended batch inference applications:

• Large-scale Data Processing: E.g., running sentiment analysis over millions of customer reviews.

• Model Fine-tuning and Evaluation: Performing evaluations across vast datasets.

• Content Generation: Generating summaries, translations, or product descriptions at scale.

• Offline Analytics: Running predictive models for trend forecasting or data enrichment.

What are benefits of distributed batch inference for LLMs?

• Save money, boost ROI: Fully load your GPUs by running tons of prompts at once, cutting down on GPU idle time (compute time you might be paying for) and lowering per-request inference costs.

• Faster processing times: Distribute the workload across multiple nodes and watch your throughput skyrocket—way quicker than using a single machine.

• Simplified architecture: Batch jobs run quietly in the background, keeping real-time systems safe from traffic spikes and ensuring everything stays steady.

Run DeepSeek r1 inference at scale on Tracto.ai with no rate limits - clone the solution notebook from Github repo and drop us a note for the API token and GPU credits.