@rohanpaul_ai: atomic[.]chat shared a revealing comparison of local open-weight LLMs running on their own hardware. They benchmarked t…
Summary
A benchmark comparison of local open-weight LLMs on a single H100 (FP8) shows DiffusionGemma is 4x faster but makes 6x more mistakes than Gemma4 26B A4B, highlighting trade-offs between speed and accuracy in diffusion versus autoregressive models.
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Cached at: 06/12/26, 02:50 AM
atomic[.]chat shared a revealing comparison of local open-weight LLMs running on their own hardware.
They benchmarked the new DiffusionGemma (diffusion text model) vs. Gemma4 26B A4B (autoregressive model) on a single H100 (FP8).
The 4X speed of DiffusionGemma changes the shape of error.
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Autoregressive models move left to right, one token at a time, which is slower, but each new word is conditioned on the exact text already written.
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Diffusion models write many tokens at once, then revise the block over several passes, so they can feel fast because the model is not waiting to finish token 1 before starting token 2.
atomic[.]chat, a desktop app for running LLMs locally
atomic.chat (@atomic_chat_hq): Diffusion Gemma is 4x faster, but makes 6x more mistakes!
We benchmarked the new diffusion LLM against its autoregressive twin on a single H100 (FP8). We gave each the same three tasks: write a Steve Jobs biography, the history of Tetris, and the story of BeOS - every next topic
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DiffusionGemma under real workloads feels very different from benchmark demos
Internal testing of DiffusionGemma reveals significant performance differences between H100 and A100 GPUs under real-world workloads, with H100s scaling much better under concurrency, and efficiency varying greatly depending on workload type, raising questions about benchmark reliability.