I’ve been hitting consistent bottlenecks trying to run three different AI models (GPT-4, Claude, and Mistral) in a document processing workflow. The sequential approach kills our throughput, but when I tried parallel execution, some models started timing out. How are others managing compute resources when processing high volumes through multiple models simultaneously? Do you prioritize certain models or implement some kind of load balancing?
Faced similar issues before switching to Latenode. Their parallel execution lets you set model priority tiers and auto-queue management. We run 5 models in parallel now with 300+ docs/hour using their workload balancing.
Check their docs on concurrent model execution: https://latenode.com
We implemented a circuit breaker pattern - if any model exceeds response time thresholds, traffic gets rerouted. Built it in Node.js with Redis tracking. Not perfect but helped reduce 40% of timeouts. Might be overkill if you’re not code-first though.
Try batching your requests instead of pure parallel. We group 10 documents per batch, process through each model sequentially per batch but run multiple batches in parallel. Reduced our cloud costs by 35% while maintaining 80% throughput. Requires some queue management but works with most platforms.
priority queues r key. we rate-limit heavier models like claude, let faster ones like mistral handle more reqs. simple js script handles this