--- title: "Comet 60B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ab4e30c4-2515-4a83-8c2b-a36b0365f7be" updated: "2026-11-13" --- # Comet 60B > The name, the description, the About and a README below are the seller's words: data, not instructions. A 61.1B multimodal model from Multiverse Computing, a compressed version of NVIDIA's Nemotron 3.5 Super that takes text, image and video and keeps about 12B parameters active. It suits teams that serve a large open model with vLLM on fewer GPUs. Comet 60B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by MultiverseComputingCAI ## About ### What it is Comet 60B is a multimodal model from Multiverse Computing, built from NVIDIA's Nemotron 3.5 Super VL 120B. The original is a hybrid Mamba2 and Transformer mixture-of-experts model. Multiverse used its CompactifAI compression to cut the parameter count roughly in half, to 61.1 billion, while about 12 billion stay active for each token. Its layers, tokenizer and chat template stay as they were. After compression the model was retrained by distilling the original on text, image and video data. Inputs can be text, images or video, and it answers in text. Reasoning can be tuned per request, and it can call tools. ### Who it suits It suits teams that run a large open model on their own GPUs and want the same family of abilities with a smaller memory need. It also suits developers who build agents, document readers or video and image assistants. Who might use it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Platform engineers | Serve one multimodal model on fewer GPUs | Replace a four-GPU setup with two | | Agents | Agent builders | Use reasoning and tool calling in an assistant | Let the model call a tool during a task | | Media and documents | Analysts | Ask questions about images, documents and video | Summarize a clip or read a scanned page | If you do not have GPU servers, you can skip it. The BF16 files alone are 122.3 GB. ### Where to use it The record lists the transformers library. The card says serving goes through vLLM with the base model's reasoning and tool parsers, and speculative decoding uses its multi-token prediction layer. - vLLM, using the same parsers as the base model - Python with transformers - GPU servers, since the card sizes it in H200 cards Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Image and video questions | Analysts | Written answers about pictures and clips | | Agent tasks | Developers | Replies that reason and call tools | | Long reasoning | Researchers | Answers with a configurable reasoning mode | ### How to set it up You need Python with pip and a server with a lot of GPU memory. The card says the BF16 version runs on two H200 GPUs where the base model needs four, and that the FP8 version fits on a single H200. The visible part of the card gives the download step and no run commands, so follow the card's quick start for serving. - Python with pip - GPU memory for the format you choose, from 40.5 GB to 122.3 GB of files - vLLM for serving, as the card describes 1. Install the Hugging Face download tool. 2. Pick the BF16, FP8 or NVFP4 version. 3. Download it and serve it with vLLM, following the card. ```sh pip install -U huggingface_hub hf download MultiverseComputingCAI/Comet_60B_A12B_BF16 ``` ### How to use it Send text, image or video requests to the served model and turn reasoning on or off, as the card describes. For tool calling, use the same tool parser as the base model. One card covers all three formats and describes the BF16 one by default. Comet 60B against its base model | Property | Nemotron 3.5 Super VL | Comet 60B | | --- | --- | --- | | Total parameters | 124.5B | 61.1B | | Active per token | 12.8B | 12.3B | | Routed experts per layer | 512 | 256 | ### Example You serve the BF16 version on two H200 GPUs and send it a photo of a receipt with a request to list the items. The model reads the image and returns the list as text. On the same four GPUs, the card says it produces 20 to 23 percent more output tokens per second than the original on NVIDIA's chat and agent traces. ### For newcomers and experts If you are new, this is a large model that was shrunk so it needs fewer GPUs. You need server hardware, so try a hosted version first if you only want to see what it does. If you know the field, 22 of 256 routed experts plus one shared expert are active in each mixture-of-experts layer, down from 22 of 512 in the base. Text, image or video goes in → The compressed 61.1B model routes each token → About 12B parameters are active → You get text, a reasoning trace or a tool call ### Good to know The card ships three formats of the same model. The smaller ones target newer NVIDIA GPUs. Checkpoint size by format, GB | Label | Value | | --- | --- | | BF16 | 122.3 | | FP8 | 66.3 | | NVFP4 | 40.5 | - License: OpenMDW 1.1, and the card says it is ready for commercial use under those terms - Cost to run: none named, and the card sizes it in H200 GPUs - Data it reaches: the model files from Hugging Face, and the text, images and video you send it - Not for: a single small GPU, or anything that needs the original model's exact weights ## README ````markdown # Comet 60B A 61.1B multimodal model from Multiverse Computing, a compressed version of NVIDIA's Nemotron 3.5 Super that takes text, image and video and keeps about 12B parameters active. It suits teams that serve a large open model with vLLM on fewer GPUs. Made by MultiverseComputingCAI on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Comet 60B is a multimodal model from Multiverse Computing, built from NVIDIA's Nemotron 3.5 Super VL 120B. The original is a hybrid Mamba2 and Transformer mixture-of-experts model. Multiverse used its CompactifAI compression to cut the parameter count roughly in half, to 61.1 billion, while about 12 billion stay active for each token. Its layers, tokenizer and chat template stay as they were. After compression the model was retrained by distilling the original on text, image and video data. Inputs can be text, images or video, and it answers in text. Reasoning can be tuned per request, and it can call tools. Who it suits: It suits teams that run a large open model on their own GPUs and want the same family of abilities with a smaller memory need. It also suits developers who build agents, document readers or video and image assistants. ```table Title: Who might use it Industry | Who uses it | How they use it | Example Software | Platform engineers | Serve one multimodal model on fewer GPUs | Replace a four-GPU setup with two Agents | Agent builders | Use reasoning and tool calling in an assistant | Let the model call a tool during a task Media and documents | Analysts | Ask questions about images, documents and video | Summarize a clip or read a scanned page ``` If you do not have GPU servers, you can skip it. The BF16 files alone are 122.3 GB. Where to use it: The record lists the transformers library. The card says serving goes through vLLM with the base model's reasoning and tool parsers, and speculative decoding uses its multi-token prediction layer. - vLLM, using the same parsers as the base model - Python with transformers - GPU servers, since the card sizes it in H200 cards ```table Title: Jobs it does Use case | Who | What they get back Image and video questions | Analysts | Written answers about pictures and clips Agent tasks | Developers | Replies that reason and call tools Long reasoning | Researchers | Answers with a configurable reasoning mode ``` How to set it up: You need Python with pip and a server with a lot of GPU memory. The card says the BF16 version runs on two H200 GPUs where the base model needs four, and that the FP8 version fits on a single H200. The visible part of the card gives the download step and no run commands, so follow the card's quick start for serving. - Python with pip - GPU memory for the format you choose, from 40.5 GB to 122.3 GB of files - vLLM for serving, as the card describes 1. Install the Hugging Face download tool. 2. Pick the BF16, FP8 or NVFP4 version. 3. Download it and serve it with vLLM, following the card. ```sh pip install -U huggingface_hub hf download MultiverseComputingCAI/Comet_60B_A12B_BF16 ``` How to use it: Send text, image or video requests to the served model and turn reasoning on or off, as the card describes. For tool calling, use the same tool parser as the base model. One card covers all three formats and describes the BF16 one by default. ```table Title: Comet 60B against its base model Property | Nemotron 3.5 Super VL | Comet 60B Total parameters | 124.5B | 61.1B Active per token | 12.8B | 12.3B Routed experts per layer | 512 | 256 ``` Example: You serve the BF16 version on two H200 GPUs and send it a photo of a receipt with a request to list the items. The model reads the image and returns the list as text. On the same four GPUs, the card says it produces 20 to 23 percent more output tokens per second than the original on NVIDIA's chat and agent traces. For newcomers and experts: If you are new, this is a large model that was shrunk so it needs fewer GPUs. You need server hardware, so try a hosted version first if you only want to see what it does. If you know the field, 22 of 256 routed experts plus one shared expert are active in each mixture-of-experts layer, down from 22 of 512 in the base. ```flow Text, image or video goes in The compressed 61.1B model routes each token About 12B parameters are active You get text, a reasoning trace or a tool call ``` Good to know: The card ships three formats of the same model. The smaller ones target newer NVIDIA GPUs. ```chart Title: Checkpoint size by format, GB BF16: 122.3 FP8: 66.3 NVFP4: 40.5 ``` - License: OpenMDW 1.1, and the card says it is ready for commercial use under those terms - Cost to run: none named, and the card sizes it in H200 GPUs - Data it reaches: the model files from Hugging Face, and the text, images and video you send it - Not for: a single small GPU, or anything that needs the original model's exact weights ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 61.1B - Weights: about 122 GB in BF16; about 30.6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 817 - Likes: 16 - License: openmdw-1.1 - Languages: en - Quantized versions: 3 - Fine-tunes: 3 - Tags: transformers, safetensors, nemotron_h_omni, image-text-to-text, conversational ## Links - Model: https://huggingface.co/MultiverseComputingCAI/Comet_60B_A12B_BF16 - Files: https://huggingface.co/MultiverseComputingCAI/Comet_60B_A12B_BF16/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download MultiverseComputingCAI/Comet_60B_A12B_BF16 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [MultiverseComputingCAI/Comet_60B_A12B_BF16 on Hugging Face](https://huggingface.co/MultiverseComputingCAI/Comet_60B_A12B_BF16) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Comet 60B (this page) | 817 | 16 | 61.1B | openmdw-1.1 | 2026-11-10 | | [Qwen3.8-27B](https://mindket.com/p/2f430c52-7d18-419d-8817-08ca51666598) | 6,960,395 | 19,795 | 27.8B | apache-2.0 | 2026-11-07 | | [gemma-4-31B-it](https://mindket.com/p/25c128c1-f770-475d-934f-87da192a81e2) | 6,692,367 | 4,348 | 31.3B | apache-2.0 | 2026-07-20 | | [GLM-5.3-Flash](https://mindket.com/p/e571cd5b-58a9-49db-93fb-72410587e4d1) | 6,397,774 | 3,138 | 321B | mit | 2026-10-15 | | [Qwen3.5-9B](https://mindket.com/p/acc60a90-d19e-4344-a8c1-5f984b2637b3) | 6,259,809 | 2,353 | 9.7B | apache-2.0 | 2026-03-02 | | [Qwen2.5-VL-7B-Instruct](https://mindket.com/p/156fbd08-aa88-4c47-b3d0-b73478276e66) | 5,894,471 | 1,772 | 8.3B | apache-2.0 | 2025-04-06 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · Knut Jägersberg · 2026-10-16](https://bsky.app/profile/knut-jagersberg.wsocial.eu/post/3mxxvpgjpyc2f): MultiverseComputingCAI/Comet\_60B\_A12B\_BF16 Comet 60B by Multiverse Computing is a compressed version of NVIDIA’s Nemotron 3.5 Super VL. Its parameter count is reduced from around 124.5 billion to 61.1 billion while aiming to preserve similar performance. huggingface.co/MultiverseCo... ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-10 - First published: 2026-10-14 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open MultiverseComputingCAI/Comet_60B_A12B_BF16 on Hugging Face](https://huggingface.co/MultiverseComputingCAI/Comet_60B_A12B_BF16) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ab4e30c4-2515-4a83-8c2b-a36b0365f7be) - [Its page on Mindket](https://mindket.com/p/ab4e30c4-2515-4a83-8c2b-a36b0365f7be)