 | we can use continuous batching for an agent swarm to actually kill research time. found performance for qwen 27b on that intel b70 32gb card. if you just chat one on one, you get: avg prompt throughput: 85.4 tokens/s avg generation throughput: 13.4 tokens/s doing 50 tasks (51200 input tokens, 25600 generated) takes 42 minutes of your life. the move is an agent swarm. 1 orchestrator and 49 agents all working at once makes the gpu swallow every prompt in the same batch. total power hits 1100 tokens a second. the quick math: single user: 42 minutes agent swarm: 70 seconds you wait about 11 seconds for the first word but the whole project finishes in 70 seconds instead of 42 minutes. it is a massive speed boost for research. stop talking to your ai and start batching it. source: https://forum.level1techs.com/t/intel-b70-launch-unboxed-and-tested/247873 :( but I don't know how to get this orchestrator and sub agent system. May be open claw will work but idk ¯\_(ツ)_/¯ . if anyone is doing this then please share your workflow. Edit : may be https://github.com/NousResearch/hermes-agent can do
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We can use continuous batching for agent swarm to drastically reduce the time for research or coding.
Reddit r/LocalLLaMA / 4/6/2026
💬 OpinionDeveloper Stack & InfrastructureSignals & Early TrendsIdeas & Deep AnalysisTools & Practical Usage
Key Points
- A Reddit post claims that using continuous batching with an “agent swarm” can massively cut time-to-completion for research or coding tasks (e.g., reducing a 42-minute run to about 70 seconds in one described workload).
- The proposed setup uses one orchestrator and many parallel agents so the GPU can process prompts in large shared batches, improving overall throughput compared with one-to-one chatting.
- Reported metrics for a Qwen 27B workload on an Intel B70 (32GB) emphasize higher aggregate throughput when tasks are parallelized, at the cost of some initial latency for the first token.
- The author suggests an implementation approach (or starting point) via an open-source agent framework (citing NousResearch/hermes-agent) but notes uncertainty about how to wire up the orchestrator/subagent workflow end-to-end.
- The post frames this as a workflow change—“stop talking” interactively and instead batch many tool-using/research sub-tasks to better utilize hardware.
