Using @CopilotKit & Google's #A2UI with local LLMs? 🤖 Following up on our article last week, here's the fully interactive, visual dashboard showcasing our benchmarks, created by @wolfmanfx: 👉 a2ui-bench.web.app/ The 3 key takeaways from our evaluation prompts 👇 (1/4)
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1️⃣ The "Small Model Cliff" 📉Models under 14B (like Llama 3.2 1B or Qwen 3 8B) scored near 0%. Surprisingly, this wasn't a reasoning failure, but format compliance. They consistently struggled to output the proper JSON format. Instruction following is the real bottleneck. (2/4)
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2️⃣ Inference Engines Shift Accuracy ⚙️The engine matters. Running identical weights on different runtimes yielded completely different semantic accuracy. Qwen 3 14B hit 90.4% accuracy on vLLM, but dropped to 68.2% on llama.cpp. It's not just a performance choice! (3/4)

May 18, 2026 · 3:54 PM UTC

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3️⃣ The Winner 🏆Gemma 4 26B MoE (NVFP4 via vLLM) is the winner. It achieved a 91.8% accuracy rate and clocked the fastest median inference speed. Read our full architectural deep dive on how the validation schema and renderers handle this: 🔗 soverius.ai/blog/behind-the-… (4/4)

A2UI, Generative UI, LLM Benchmark, Local LLMs, DGX Spark, vLLM, llama

Can local LLMs produce valid A2UI? I benchmarked 14 model and engine combos on a DGX Spark. The 14B cliff and the failure modes are not what you'd guess.

soverius.ai
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