@svpino: For the first time, I feel open-weight models are impossible to ignore. We are at a point where these models are compet…
Summary
Santiago (@svpino) highlights MiniMax-M2.7, a 230B open-weight model that rivals top proprietary models like Opus 4.6 and GPT-5.4, achieving 440+ tokens/s inference on SambaNova at low cost.
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Dario: “With open source you can see the source code. Here we can’t see inside the model.” My 0.646 t/s MiMo-2.6-Pro crammed thru 128GB of Jetson AGX Thor: “Yeah, well, that’s just, like, your opinion, man.”
A tinkerer jokingly rebuts Dario Amodei's critique of open-weight models by running MiMo-2.6-Pro (FP8, ~566GB) on a Jetson AGX Thor with 128GB memory at 0.646 tokens/sec — painfully slow, but fully traced and replayable step by step.
@with_gene2626: wtf? 397b nvfp4 is a 3 or 4 spark setup... showing better benchmarks then all of the current open weight models? anyone…
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@stevibe: MiniMax M2.7 is 230B params. Can you actually run it at home? I tested Unsloth's UD-IQ3_XXS (80GB) on 4 different rigs:…
A user tested MiniMax M2.7 (230B parameter model) using Unsloth's UD-IQ3_XXS quantization (80GB) across four different hardware configurations including RTX 4090, RTX 5090, RTX PRO 6000, and DGX setups, reporting token generation speeds and time-to-first-token metrics.
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