--- title: "Kimi-K3.1-Pro-Coder" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ffa7bc66-e307-4674-8466-5e7d191cbfa9" updated: "2026-11-12" --- # Kimi-K3.1-Pro-Coder > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs a heavily compressed copy of the 2.8T-parameter Kimi-K3.1-Pro, tuned for coding and math. Made by Grawnch with a three-tier mixed-bit scheme, for people with high-end GPU workstations. Kimi-K3.1-Pro-Coder is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Grawnch ## About ### What it is Kimi-K3.1-Pro-Coder is a compressed copy of moonshotai/Kimi-K3.1-Pro, a 2.8T-parameter mixture-of-experts model, made by Grawnch. A three-tier mixed-bit scheme called Strata-Coder was tuned on how often experts fire during coding and math work. The result is a set of 96 shards totaling 774.97 GB, where busy experts keep more bits and rarely used ones are squeezed hardest. ### Who it suits People with high-end hardware who want an enormous coding-focused model in fewer resources, and researchers who study compression of mixture-of-experts models. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers with GPU workstations | Run a huge coding model with weights split over GPU memory, system RAM and NVMe | Solving a LeetCode-style problem | | Research | Quantization researchers | Study mixed-bit compression of expert layers | Comparing the card's agreement and perplexity figures | | AI platforms | Infrastructure engineers | Test tiered offloading with the Strata engine | Checking throughput on one card | Anyone without a workstation-class GPU can skip it, since the card's tests ran on a card with 102.6 GB of VRAM. ### Where to use it - The author's Strata engine, a loader that spreads weights over GPU memory, system RAM and an NVMe drive - The author's dr-triton kernels for fused Triton dequantization - NVIDIA RTX PRO 6000 Blackwell Server Edition, the card used for the card's tests - Windows, macOS or Linux with Python, for the download | Use case | Who | What they get back | | --- | --- | --- | | Coding problems | Developers | Solutions to programming tasks from a coder-tuned model | | Math problems | Students and researchers | Integer answers to competition-style questions | | Offloading tests | Infrastructure engineers | A look at tiered loading across VRAM, RAM and NVMe | ### How to set it up - Python and the huggingface\_hub package - The Strata engine and the dr-triton kernels, both named in the card - A large-VRAM GPU, plenty of system RAM and fast NVMe storage 1. Install Python and the huggingface\_hub package with the first command below. 2. Download the model files with the second command, which is a huge transfer. 3. Get the Strata engine and the dr-triton kernels from the author. 4. Load the model through Strata so the three storage tiers are used. ```sh pip install -U huggingface_hub hf download Grawnch/Kimi-K3.1-Pro-Coder ``` ### How to use it The card shows no run command. It points to the Strata engine for loading. That engine keeps the hottest experts in GPU memory and a warm set in pinned system RAM. The cold tail stays on an NVMe drive and is read through mmap. | Tool | What it does | | --- | --- | | Strata | Loads the model across VRAM, RAM and NVMe with router lookahead | | dr-triton | Provides fused Triton kernels that dequantize the weights | ### Example You give it a competitive programming problem of the kind in the card's LiveCodeBench set. On the card's rolling-window set of 30 problems, the base model and this copy each solved 29 of them. ### For newcomers and experts This is an advanced model that needs special loading software and a lot of hardware, so a first step is to read the card and note which pieces you would need. Skip the download until you have them. The base model has 93 layers with 896 routed experts per layer. Non-routed parts, including attention and the vision tower, stay in BF16 or F32. The routed experts are split by use into three tiers of 15, 30 and 55 percent. The card reports about 0.02 mean KL divergence from the base and a perplexity rise of roughly one to three percent. Rank experts by coding and math use → Assign each expert to a tier → Compress cold experts with fewer bits → Load hot, warm and cold tiers apart → Generate with Strata ### Good to know - License: Apache 2.0 - Cost to run: no paid service named, but you need a high-end GPU, system RAM and fast NVMe storage - Data it reaches: the download fetches files from Hugging Face, and the Strata and dr-triton projects are separate downloads - The card's tests are the author's own and use only 30 problems per set, so treat them as hints - The card gives 774.97 GB for the full set, while Hugging Face's record lists about 1,270 GB Size by tier in GB | Label | Value | | --- | --- | | Non-routed | 114.76 GB | | Tier 1 hot | 160.58 GB | | Tier 2 warm | 254.53 GB | | Tier 3 cold | 244.95 GB | Effective bits per weight by expert tier | Label | Value | | --- | --- | | Tier 1 hot | 2.81 bpw | | Tier 2 warm | 2.56 bpw | | Tier 3 cold | 1.44 bpw | ## README ````markdown # Kimi-K3.1-Pro-Coder Runs a heavily compressed copy of the 2.8T-parameter Kimi-K3.1-Pro, tuned for coding and math. Made by Grawnch with a three-tier mixed-bit scheme, for people with high-end GPU workstations. Made by Grawnch 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: Kimi-K3.1-Pro-Coder is a compressed copy of moonshotai/Kimi-K3.1-Pro, a 2.8T-parameter mixture-of-experts model, made by Grawnch. A three-tier mixed-bit scheme called Strata-Coder was tuned on how often experts fire during coding and math work. The result is a set of 96 shards totaling 774.97 GB, where busy experts keep more bits and rarely used ones are squeezed hardest. Who it suits: People with high-end hardware who want an enormous coding-focused model in fewer resources, and researchers who study compression of mixture-of-experts models. ```table Industry | Who uses it | How they use it | Example Software | Developers with GPU workstations | Run a huge coding model with weights split over GPU memory, system RAM and NVMe | Solving a LeetCode-style problem Research | Quantization researchers | Study mixed-bit compression of expert layers | Comparing the card's agreement and perplexity figures AI platforms | Infrastructure engineers | Test tiered offloading with the Strata engine | Checking throughput on one card ``` Anyone without a workstation-class GPU can skip it, since the card's tests ran on a card with 102.6 GB of VRAM. Where to use it: - The author's Strata engine, a loader that spreads weights over GPU memory, system RAM and an NVMe drive - The author's dr-triton kernels for fused Triton dequantization - NVIDIA RTX PRO 6000 Blackwell Server Edition, the card used for the card's tests - Windows, macOS or Linux with Python, for the download ```table Use case | Who | What they get back Coding problems | Developers | Solutions to programming tasks from a coder-tuned model Math problems | Students and researchers | Integer answers to competition-style questions Offloading tests | Infrastructure engineers | A look at tiered loading across VRAM, RAM and NVMe ``` How to set it up: - Python and the huggingface_hub package - The Strata engine and the dr-triton kernels, both named in the card - A large-VRAM GPU, plenty of system RAM and fast NVMe storage 1. Install Python and the huggingface_hub package with the first command below. 2. Download the model files with the second command, which is a huge transfer. 3. Get the Strata engine and the dr-triton kernels from the author. 4. Load the model through Strata so the three storage tiers are used. ```sh pip install -U huggingface_hub hf download Grawnch/Kimi-K3.1-Pro-Coder ``` How to use it: The card shows no run command. It points to the Strata engine for loading. That engine keeps the hottest experts in GPU memory and a warm set in pinned system RAM. The cold tail stays on an NVMe drive and is read through mmap. ```table Tool | What it does Strata | Loads the model across VRAM, RAM and NVMe with router lookahead dr-triton | Provides fused Triton kernels that dequantize the weights ``` Example: You give it a competitive programming problem of the kind in the card's LiveCodeBench set. On the card's rolling-window set of 30 problems, the base model and this copy each solved 29 of them. For newcomers and experts: This is an advanced model that needs special loading software and a lot of hardware, so a first step is to read the card and note which pieces you would need. Skip the download until you have them. The base model has 93 layers with 896 routed experts per layer. Non-routed parts, including attention and the vision tower, stay in BF16 or F32. The routed experts are split by use into three tiers of 15, 30 and 55 percent. The card reports about 0.02 mean KL divergence from the base and a perplexity rise of roughly one to three percent. ```flow Rank experts by coding and math use Assign each expert to a tier Compress cold experts with fewer bits Load hot, warm and cold tiers apart Generate with Strata ``` Good to know: - License: Apache 2.0 - Cost to run: no paid service named, but you need a high-end GPU, system RAM and fast NVMe storage - Data it reaches: the download fetches files from Hugging Face, and the Strata and dr-triton projects are separate downloads - The card's tests are the author's own and use only 30 problems per set, so treat them as hints - The card gives 774.97 GB for the full set, while Hugging Face's record lists about 1,270 GB ```chart Title: Size by tier in GB Non-routed: 114.76 GB Tier 1 hot: 160.58 GB Tier 2 warm: 254.53 GB Tier 3 cold: 244.95 GB ``` ```chart Title: Effective bits per weight by expert tier Tier 1 hot: 2.81 bpw Tier 2 warm: 2.56 bpw Tier 3 cold: 1.44 bpw ``` ## On Hugging Face, 13 November 2026 - Task: image text to text - Parameters: 1.3T - Weights: about 1,270 GB in U8 - Formats: Safetensors - Downloads in the last 30 days: 695 - Likes: 6 - License: apache-2.0 - Based on: moonshotai/Kimi-K3.1-Pro - Tags: safetensors, kimi_k31, kimi-k3.1, quantization, mixed-bit, strata, dr-triton, image-text-to-text, 8-bit ## Links - Model: https://huggingface.co/Grawnch/Kimi-K3.1-Pro-Coder - Files: https://huggingface.co/Grawnch/Kimi-K3.1-Pro-Coder/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Grawnch/Kimi-K3.1-Pro-Coder ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Grawnch/Kimi-K3.1-Pro-Coder on Hugging Face](https://huggingface.co/Grawnch/Kimi-K3.1-Pro-Coder) ## 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 | | --- | --- | --- | --- | --- | --- | | Kimi-K3.1-Pro-Coder (this page) | 695 | 6 | 1.3T | apache-2.0 | 2026-11-09 | | [DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata](https://mindket.com/p/6a2502f6-4851-488e-b942-b3e466f51991) | 22,196 | 49 | 14.7B | mit | 2026-10-19 | | [Strata-Sage-A1.5-GGUF](https://mindket.com/p/01ef9451-54aa-49d7-8214-afc7bf42e6d6) | 5,466 | 14 | 35.5B | apache-2.0 | 2026-11-11 | | [Qwen3.8-27B-QUASAR-NVFP4](https://mindket.com/p/fe29aed2-fb2d-401b-b1d0-c9eb63db3f4a) | 119,414 | 164 | 27.8B | apache-2.0 | 2026-10-15 | | [Qwen3.6-35B-A3B-2Bit-GSQ](https://mindket.com/p/1419ceb9-6ab2-48e2-aec8-e0401259317e) | 974 | 37 | 35.5B | apache-2.0 | 2026-08-14 | | [Qwen3.8-27B-NVFP4 Deployment Edition](https://mindket.com/p/b0088469-7611-4dc4-8ce5-3fbb79dd4d39) | 190 | 2 | 14.6B | apache-2.0 | 2026-11-02 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-11-09 - First published: 2026-11-08 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Grawnch/Kimi-K3.1-Pro-Coder on Hugging Face](https://huggingface.co/Grawnch/Kimi-K3.1-Pro-Coder) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ffa7bc66-e307-4674-8466-5e7d191cbfa9) - [Its page on Mindket](https://mindket.com/p/ffa7bc66-e307-4674-8466-5e7d191cbfa9)