Nemotron 3.5 Lightning

Developers testing on consumer hardware (M1 Max 64GB, llama.cpp, LM Studio) consistently praise raw inference speed: ~50-135 tokens/sec without MTP, with MTP providing additional structured-text boosts. One HN user explicitly compared: '27B hovers around 42 ts, 35B around 135 ts' at 10k context. However, coding quality is sharply divisive. A developer testing MoE models (including Nemotron) on a collaborative whiteboard task in Cloudflare OS found them 'terrible' — 'couldn't get the job done at all, went way off the rails.' Conversely, another user reported it 'writes pretty good code' on a WordPress plugin task but 'second-guesses itself in thinking traces.' A third called it an 'over-thinker' that produced four SVG reasoning traces before delivering a 'bad' pelican-on-bicycle. On benchmarks vs reality: users note it looks 'behind the qwen equivalent model on most benchmarks' but describe it as 'less benchmaxxed / stubborn,' suggesting better generalization to unfamiliar task types. Multiple users praised the fully open-source training pipeline: 'I don't think another model this performant exists with fully open source data and recipes alongside the weights.' Anti-corporate sentiment surfaced: 'Nvidia just throwing something for peasants... Make 1TB DGX priced affordably, not some crap model.' The NVFP4 quantized variant, multiple model variants (5 total on HuggingFace), and experimental Switchyard router were discussed, with the latter explicitly marked 'Not for production use.'

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