Two point seven eight trillion, zero downloads

The Hugging Face model API is unambiguous: created 17 August 2026, pinned at revision b3a52d265b56551c0011b24d299ba3f8f1393e42, 96 safetensors shards, about 1.56 TB of used storage, zero downloads, zero likes, zero discussions. The repo it derives from—moonshotai/Kimi-K3—has 2.16 million downloads and 10,800 likes. The gap between those two numbers is the whole story.

The card text is not this repo's own. Every badge, logo and code example references SHS-Lab/Kimi-K3-Abliterated, and the file tree carries a copied assets/shs-lab-logo.png. This is a re-upload of SHS-Lab's card and, presumably, its weights. What makes it worth a field note is the number on the tin.

What 2.78 trillion parameters means in memory

2,779,931,837,184 parameters. In FP16 that is roughly 5.1 TiB—no single node holds that. The repo does not ship FP16: the safetensors manifest shows 2,722,740,830,208 U8 elements against 57.2 billion BF16 and a trace of F32, which is compressed-tensors MXFP4 packing—4-bit weights, two per byte, with attention, shared experts and a few families left unquantized. That lands at roughly 1.35 TiB of weights, matching the reported storage.

Now the arithmetic. One H200 carries 141 GB of HBM; the weights alone need about eleven of them with zero headroom. Sixteen H200s give ~2.1 TiB aggregate, and after ~1.35 TiB of weights you are left with under a TiB for activations and KV cache—a number the 1-million-token context will happily consume. Our managed-price ballpark is 16× H200 at about $87.20/hour, labeled approximate managed price estimate—highly speculative. The repo carries the endpoints_compatible tag, but no inference provider lists it. This model is not practically hostable today.

Why "modal"?

The suffix is never explained in the card. The repo's pipeline tag is any-to-any (the upstream base is image-text-to-text), and the card claims text, image and video in one architecture with a 401M MoonViT-V2 vision encoder. "Modal" reads as shorthand for multimodal/any-to-any—the one thing about this upload that is not inherited from SHS-Lab is the name. The video claim deserves a flag: the upstream card lists text and image; video comes only from the copied SHS-Lab text.

Moonshot's Kimi K3, briefly

All of the following are upstream claims, not our measurements: a 2.8T-parameter MoE on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), 93 layers (69 KDA + 24 Gated MLA), 896 experts with 16 routed plus 2 shared, a 1-million-token context, and quantization-aware training from SFT with MXFP4 weights and MXFP8 activations. Moonshot calls it the world's first open 3T-class model and claims a ~2.5× scaling-efficiency gain over Kimi K2. Weights landed in July 2026—the HF repo was last modified 27 July—and the release drew standard tech-media coverage, from Tom's Hardware ("2–3× easier to run"—the 104B-active claim, not a small load) to Fireship. Official docs live at the Kimi tech blog. The Kimi K3 License is a custom "other" license the derivative inherits unchanged.

Who actually attempts a K3 abliteration

Plenty of people—abliterating a 2.78T model is cheap; running it is not. The HF search for Kimi-K3-Abliterated returns a healthy ecosystem: Uniboshi/Kimi-K3-Abliterated-V1 (2,449 downloads, 78 likes), Blackfrost-AI's gated Q2_K GGUF (8,746 downloads), penclaw's GGUF (204 likes), SHS-Lab's original (95 downloads), plus rbinrs, 0xpwdz, kepom and GrEarl. Community appetite is documented: an August 2026 r/LocalLLaMA thread is literally titled "Waiting for someone to abliterate Kimi K3 and host it so we can use it through API"—community opinion, and note the ask: host it. Nobody is.

The method is the standard refusal-direction edit from Arditi and colleagues: estimate a direction associated with refusal from activation comparisons, then project it out of selected weights. No training data, no gradients. SHS-Lab's card (copied here verbatim) claims more than 98% of identified refusal signal removed for English and Japanese. That is a publisher claim for SHS-Lab's artifact; we cannot verify this re-upload's weights even match SHS-Lab's edits without hashing 1.5 TB. The "Resggg" handle is not identified by any primary source we found, so we credit the handle and nothing more.

What is proven here?

The exact artifact is inspectable: pinned revision, 96 shards, quant config. Architecture facts come from Moonshot's upstream card. The "98%+ refusal signal removed" claim, the video modality and the abliteration details are SHS-Lab publisher claims copied into this repo. Zero downloads means zero independent verification exists—not a single run report.

The honest gap between uploaded and runnable

Even the most-downloaded K3 abliterations sit in the single-digit thousands, dominated by Q2_K GGUF experiments on workstation-class hardware—a much cruder object than this repo's MXFP4. Abliteration is the cheap part of this stack: a weight edit that preserves architecture. The runnability problem is inherited wholesale from the base model. If you want an uncensored Kimi today, your realistic options are the upstream Kimi API or waiting for someone to stand up a real deployment. This repo is a symbolic artifact: uploaded, licensed, endpoints-compatible, and entirely unrun.

How we treat it

We do not host it, and this is a field note rather than a catalog route. No benchmark transfer from the upstream card, no endorsement of copied claims, and no assumption that the re-uploaded weights are byte-identical to SHS-Lab's edits. "Abliterated" means refusal-reduced per publisher claim—not verified, not zero-refusal, and not even run once.

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