Conclusion

Congratulations! You’ve completed the WordSwarm AI workshop.

What you accomplished

  • Module 1: Reasoning Prompting — Explored /think and /no-think to control LLM reasoning behavior, measured the latency-quality tradeoff, and watched reasoning models outperform non-reasoning models on the WordSwarm game

  • Module 2: GuideLLM Benchmarking — Benchmarked four models across different architectures and GPU allocations, compared synthetic throughput with real-world game performance, and explored concurrency profiles

Key takeaways

Reasoning matters for complex tasks

kimi-k2-5 dominates the WordSwarm leaderboard because it reasons through ambiguity. Smaller models are faster but can’t follow a game loop reliably. The /think and /no-think tags give you direct control over this tradeoff.

Benchmarks vs. reality

Synthetic benchmarks measure raw inference capacity — tokens/sec, latency percentiles, TTFT. Real-world performance depends on how well the model uses those tokens. nemotron-cascade has the highest raw throughput in-game (128.6 t/s) but scores 4x lower than kimi-k2-5.

GPU cost-efficiency varies

Model GPU Memory Best Score Memory per Point

kimi-k2-5

1,128 GB

1,038

1.09 GB/pt

nemotron-cascade-2-30b

71 GB

264

0.27 GB/pt

llama-4-scout-17b

142 GB

153

0.93 GB/pt

llama-3.2-3b-instruct

18 GB

36

0.50 GB/pt

nemotron-cascade is the most GPU-efficient scorer at 0.27 GB per point. kimi-k2-5 wins on absolute score but needs 1 TB of HBM3e for a word game.

Try it yourself

  • Play the game: https://red.ht/wordswarm

  • Watch the agent: Select a model, click START AGENT, and see how different models approach the same puzzle

  • Beat the AI: No human scores on the production leaderboard yet — can you beat kimi-k2-5’s 1,038?

Resources

Thank you!

We hope you enjoyed this workshop. The combination of reasoning prompting and model benchmarking gives you practical tools for evaluating and deploying LLMs on your own MaaS infrastructure.