How Zerve’s Fleet Simplifies Distributed Computing and Parallelism
LLMs can do incredible things. Generate text. Summarize documents. Analyze sentiment. But when you need to process thousands of items or run hundreds of variations simultaneously, things get messy fast.
Most workflows start small. A single prompt. A few test cases. A couple of results. Then reality hits. You need to process thousands of records, test hundreds of variations, or train a model on real customer data. That is when the bottlenecks start piling up.
You can either run everything one step at a time and wait, or start from scratch and rewrite everything to handle multiprocessing. Maybe spin up extra compute, split the inputs into batches, and hope the coordination does not fall apart. Not a scalable or sustainable approach.
Where scaling gets stuck
The moment you move from a few examples to production scale work, things get complicated.
Running jobs one by one is painfully slow
Scaling often means bolting on extra tools just to distribute workloads
Complex pipelines with multiple languages or frameworks add overhead
Teams end up spending more time building workarounds than building solutions.
Parallelism without the headaches
The Fleet changes that. Instead of writing complex multiprocessing code or managing batch jobs manually, you add one function: spread.
Zerve spins up compute automatically and runs jobs in parallel
You can test your logic with a single input before scaling up
Results are aggregated and flow to the next step in the workflow
You do not need to rewrite your logic. You do not need to set up extra infrastructure. Your work runs at scale without extra effort.
Everything stays inside Zerve. Compute happens in your environment, whether cloud or on prem. You keep full control of data and infrastructure.
Why it matters
When you are running batch prompts, evaluating model outputs, or iterating on GenAI workflows, this is the difference between waiting hours and getting results on the spot.
With the Fleet, you are not stuck waiting on loops or fighting with multiprocessing. You or the Zerve Agent can focus on improving models, fine tuning results, and getting work done without worrying if the setup can handle it.
The bottom line
Scaling GenAI workloads should not mean rebuilding them from scratch. The Fleet lets you keep your existing workflows, your language of choice, and your infrastructure.
It just runs faster. One line of code. Full speed. No tradeoffs.
Related resources
FAQ Section
What problem does the Fleet solve?
It removes bottlenecks when scaling GenAI and LLM workloads by providing automatic parallelism without custom multiprocessing code or extra orchestration tools.
How do I use the Fleet?
Add the spread function to fan out work. Zerve allocates compute, runs tasks in parallel, aggregates results, and continues your workflow.
Do I need to rewrite my existing logic?
No. You can validate on a single input, then scale up with the same code.
Where does the compute run?
Inside your environment. You can run in your cloud or on prem while keeping full control of data and security.
What workloads benefit most?
Batch prompting, multi variant evaluation, document processing, data enrichment, and model training.
How does this compare to DIY multiprocessing?
Fleet avoids the coordination overhead. You do not manage workers, queues, retries, or batching. The platform handles it for you.
