1.2B Local Model Beats 1T Clouds in Poker: Aggression Trumps Knowledge in Shove-or-Fold Format
A developer ran 6 LLMs through 5 Texas Hold'em tournaments on a 16GB MacBook using a custom framework (Hive). The lineup: Liquid lfm2.5 (1.2B, LM Studio, ~5s/decision), Qwen3 (1.7B, LM Studio, ~2.5 min), Claude Haiku 4.5, GPT-OSS (120B, Fireworks), MiniMax M2 (230B, Fireworks), and Kimi K2 (~1T, Fireworks). Locals ran sequentially due to RAM limits.
Results
- Tournament 1: Qwen (1.7B local)
- Tournament 2: MiniMax (230B cloud)
- Tournament 3: Liquid (1.2B local)
- Tournament 4: Kimi (~1T cloud)
- Tournament 5: Liquid (1.2B local)
Run 3 highlighted the dynamic: Liquid played 6 hands with 19 raises and 0 folds, turning a $1M starting stack into $5.98M. Meanwhile, GPT-OSS (120B) executed 0 raises and 5 folds in 6 hands, getting blinded out. The format (25 hands, 5K/10K blinds + 1K ante) is effectively shove-or-fold, rewarding aggression over theoretical poker skill.
Key Insight
Liquid doesn't recognize bad hands, so it raises everything. Against opponents that fold too often, this prints money. The author notes: "Not claiming small models are smarter at poker. In this specific format, not knowing when to fold is an advantage." Larger models 'understand' poker enough to fold weak hands, but in a short-stack tournament, patience is punished.
What's Next
Plans include longer tournaments (100+ hands, lower blinds) where hand-reading matters. The framework supports custom personas (personality traits, risk tolerance, fears). Requests for Mistral, Llama, Gemma 3 are welcome. Code and full result JSONs are on GitHub: https://github.com/chiruu12/Hive (hive-arena/ for runner, tournaments/results/ for data).
📖 Read the full source: r/LocalLLaMA
👀 See Also
NYC & LAUSD Impose Strict AI Bans in Schools: How It Impacts Devs and EdTech
NYC banned AI in K-8 classes, LAUSD imposed a 1-year moratorium on AI use for all students. Here's the breakdown for developers building educational tools.
Macs for Local LLM and OpenClaw: Prompt Processing Bottleneck Makes Cloud Cheaper
A developer shares that Macs are slow for prompt processing compared to Nvidia GPUs, making cloud models like Deepseek more cost-effective for AI agents unless privacy requires local inference.
Amazon S3 Annotations: 1GB Metadata per Object for AI Agent Workflows
AWS announces S3 annotations — up to 1,000 annotations per object, each up to 1 MB, totaling 1 GB. Mutable, queryable via Athena, no retrieval fees for Glacier objects.
SDL Project Bans AI-Written Commits in Response to GitHub Issue
The SDL project has implemented a policy banning AI-generated commits after a GitHub issue raised concerns about Copilot usage in code reviews. The issue specifically mentions reviews #13277 and #12730 as examples where AI assistance was detected.