You're burying the lede: SOTA 'Reasoning Models' (o1/GPT-4) are actually unusable for agent swarms because inference latency kills the recursion loop.
The real alpha here is Parallel Consensus. Running 5 Llama-3 instances via vLLM to critique each other at <200ms TTFT (Time To First Token) beats a single, slow GPT-4 wrapper every time.
Error correction belongs in the orchestration layer, not the model weights. Is the 'One Giant Model' era finally over for agents?
Spot on. We found that ensembles of small models often beat a single large model.
The catch is VRAM. You can't run parallel swarms efficiently without PagedAttention. We rely on vLLM to share the KV cache for the system prompt—otherwise, spinning up 5 agents for a consensus vote would instantly OOM the GPU.
Hi HN,
We built a platform for orchestrating multi-agent debates (e.g., "Security" vs. "Refactoring" experts).
The Challenge: Standard sequential agent chains (A -> B -> C) are too slow for real-time chat.
The Fix (vLLM): We built a custom inference layer on top of vLLM to solve the bottleneck:
Parallelism: We use continuous batching to generate multiple agent responses simultaneously rather than waiting for sequential turns.
Memory: PagedAttention allows our agents to share the KV cache for the common context/system prompts, drastically reducing VRAM usage.
We’d love feedback on the responsiveness. Create an expert, start a debate, and let us know if the parallel inference makes the conversation feel fluid enough.
I'm skeptical. vLLM is a throughput engine, not a latency engine. For small batches, TensorRT-LLM smokes it.
Also, 'parallel inference' implies a race condition on the context window. If Agent A and B generate simultaneously based on stale state, aren't you just generating 5 divergent hallucinations at once? How do you resolve the merge conflict?
Thanks for the feedback—that's a solid critique.
You're right that TRT-LLM wins on raw latency, but we chose vLLM for the flexibility to hot-swap LoRA adapters dynamically. Regarding the 'race condition': we actually view that divergence as a feature to prevent sycophancy (agents biasing each other). It’s effectively Map-Reduce for conversation.