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- a model that feels less censored than stock chat releases - a model that is more capable than the raw base on practical text workloads - a compact local GGUF that still serves quickly on Apple Silicon - Uncensored chat behavior without forcing every prompt into coding mode - Tuned from the strongest `fast` line instead of the raw base - Neutral chat template baked into the GGUF to reduce prompt-routing bugs - Verified on Apple Silicon with clean general-chat and coding responses - Base model: `google/gemma-4-26B-A4B-it` - Format: `GGUF Q4_K_M` - General Korean prompt speed: `222.0 tok/s` - Generation speed: `89.4 tok/s` - Derived from the verified `SuperGemma Fast` MLX line

Text generation
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**A one-DGX-Spark Q4_K_M release with a native MTP draft, Q8_0 vision projector, bounded reasoning, and a verified 262K context path.** This is the llama.cpp build of `Jiunsong/SuperQwen3.8-27b-abliterated`: the corrected rank-4 OBLITERATUS model, not an adapter and not the earlier incorrect lineage. The target, MTP draft, and multimodal projector are separate so text-only users do not pay for the projector, while vision and native speculative decoding remain available. The target contains 433 Q4_K, 65 Q6_K, and 353 F32 tensors. The MTP draft contains 10 Q4_0 and 8 F32 tensors. The projector deliberately retains unsupported or sensitive matrices: 83 Q8_0, 27 F16, and 224 F32 tensors.

Visual QA
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1 M = 1 million tokens · USD
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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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