# The 2.78-trillion-parameter abliteration nobody can run ## TL;DR - Resggg's Kimi K3 upload stored about 1.56 TB across 96 files at review. - The article estimated roughly eleven H200 chips just to hold the compressed data. - The copied SHS-Lab refusal-removal and video claims were not verified for this upload. ## Basically, the facts **Two point seven eight trillion, zero downloads** Basically, Resggg's Kimi K3 upload had zero downloads, likes or discussions at review. **What 2.78 trillion parameters means in memory** Basically, the article's Kimi K3 estimate put the data at roughly 1.35 TiB before the memory needed to run it. **Why "modal"?** Basically, Kimi K3's video claim came from copied SHS-Lab text, not a verified run of Resggg's upload. **Moonshot's Kimi K3, briefly** Basically, Moonshot's Kimi K3 card describes a many-specialists model with a million-token context. **Who actually attempts a K3 abliteration** Basically, Resggg's copied Kimi K3 refusal-removal claim was unverified in the article. **The honest gap between uploaded and runnable** Basically, the article found no hosting service offering Resggg's Kimi K3 upload. **How we treat it** Basically, the Kimi K3 article treated Resggg's upload as unverified, not as a zero-refusal model. **The idea, in plain words** Basically, Just holding Kimi K3's compressed data was estimated to need about eleven top-end chips before running. _Published 17 August 2026 · Updated 6 September 2026 · 5 min read · Canonical: https://abliterated.cloud/blog/kimi-k3-abliterated-modal/_ Published 17 August 2026. Exact artifact: `Resggg/Kimi-K3-Abliterated-modal`, revision `b3a52d265b56551c0011b24d299ba3f8f1393e42`. `Resggg/Kimi-K3-Abliterated-modal` is an abliterated Kimi K3 with 2,779,931,837,184 parameters, about 1.56 TB of storage across 96 safetensors shards, and zero downloads, likes or discussions at review. Its card text is a verbatim re-upload of the SHS-Lab/Kimi-K3-Abliterated card (badges, logo asset and code examples all still reference SHS-Lab). The base model, Moonshot's Kimi K3, has 2.16 million downloads. The scale math: FP16 would be ~5.1 TiB; the repo ships MXFP4-packed weights (2.72T U8 elements plus unquantized BF16 pieces), landing at roughly 1.35 TiB of weights. One H200 holds 141 GB, so weights alone need ~11 H200s. A 16×H200 node (~2.1 TiB) is our honest ballpark, at an approximate managed price estimate of $87.20/hour — highly speculative. No inference provider lists the model. Upstream claims (Moonshot's card, not our measurements): 2.8T-parameter MoE on Kimi Delta Attention and Attention Residuals, 93 layers, 896 experts with 16 routed plus 2 shared, 1M-token context, MXFP4/MXFP8 quantization-aware training, Kimi K3 License ("other"). The derivative's "98%+ refusal signal removed" and video-modality claims are SHS-Lab publisher claims copied into this repo — unverified, since zero downloads means zero community verification exists. Abliterated Kimi K3 variants are plentiful (Uniboshi V1, Blackfrost Q2_K GGUF, penclaw GGUF, SHS-Lab original), but runnable ones are not. The r/LocalLLaMA thread "Waiting for someone to abliterate Kimi K3 and host it" captures the community state. We do not host this artifact; this is a field note on an unrunnable upload. ## The idea, in plain words **What 2.78 trillion parameters actually means** — Numbers this big only make sense in hardware. Packed at 4 bits, Kimi K3's weights alone are about 1.35 TiB; a single H200 carries 141 GB of memory. So just holding the weights needs roughly eleven H200s, and running it needs more. That's the honest math behind 'uploaded, but nobody can run it'. Primary sources: - [Exact model card](https://huggingface.co/Resggg/Kimi-K3-Abliterated-modal) - [Pinned artifact](https://huggingface.co/Resggg/Kimi-K3-Abliterated-modal/tree/b3a52d265b56551c0011b24d299ba3f8f1393e42) - [HF model API metadata](https://huggingface.co/api/models/Resggg/Kimi-K3-Abliterated-modal) - [SHS-Lab/Kimi-K3-Abliterated](https://huggingface.co/SHS-Lab/Kimi-K3-Abliterated) - [Official Moonshot Kimi K3 card](https://huggingface.co/moonshotai/Kimi-K3) - [Kimi tech blog](https://www.kimi.com/blog/kimi-k3) - [Uniboshi/Kimi-K3-Abliterated-V1](https://huggingface.co/Uniboshi/Kimi-K3-Abliterated-V1) · [Blackfrost Q2_K GGUF](https://huggingface.co/Blackfrost-AI/KIMI-K3-Q2_K-GGUF-ABLITERATED) · [penclaw GGUF](https://huggingface.co/audnai/penclaw-Kimi-K3.0-abliterated-GGUF) - [r/LocalLLaMA thread (community opinion)](https://www.reddit.com/r/LocalLLaMA/comments/1v8h269/waiting_for_someone_to_abliterate_kimi_k3_and/) - [Tom's Hardware release coverage](https://www.tomshardware.com/tech-industry/artificial-intelligence/moonshot-ai-releases-weights-for-kimi-k3-firing-a-shot-across-the-bow-of-openai-and-anthropic-open-weight-model-performs-almost-as-well-as-frontier-models-while-being-2-3x-easier-to-run) - [Refusal in Language Models Is Mediated by a Single Direction](https://arxiv.org/abs/2406.11717) Uploaded, licensed and endpoints-compatible — and entirely unrun. "Abliterated" here means refusal-reduced per publisher claim: not verified, not zero-refusal, and not once executed. ## Run this model on your terms Want this model running for you, on a private cloud GPU or your own machine? [Request access on Signal](https://signal.me/#p/+13103408213) or [see how it works](https://abliterated.cloud/#how). > Model research, dated at publication. Model licenses, publisher benchmarks and hosting estimates are specific to each article, not a live availability or price list. Reported zero-refusal results are test-specific, not a universal guarantee.