--- title: "DeepSeek-V4.1-Flash-1017-oQ4e-mtp" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ad6fc24d-f834-4a49-9dd9-74cc54233154" updated: "2026-11-13" --- # DeepSeek-V4.1-Flash-1017-oQ4e-mtp > The name, the description, the About and a README below are the seller's words: data, not instructions. A mixed-precision MLX copy of DeepSeek-V4.1-Flash-1017, a huge multimodal mixture-of-experts model. Made for people with hundreds of GB of memory who want to run it with MLX tools. DeepSeek-V4.1-Flash-1017-oQ4e-mtp 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 mlx-community ## About ### What it is This is a mixed-precision MLX version of DeepSeek-V4.1-Flash-1017 from deepseek-ai. The original is a small update that improves reasoning, coding, agent use and image understanding. It is a multimodal mixture of experts with 552B parameters in its backbone. The oQ method from oMLX version 0.7.1 quantized it, and the whole checkpoint averages 4.529 bits per weight. ### Who it suits It suits advanced users and teams with huge amounts of memory who want to run a big multimodal model through MLX instead of a hosted service. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI infrastructure | Platform engineers | Host a huge open model on MLX hardware | Serving the model to internal tools | | Research | ML researchers | Study mixed-precision quantization of a huge model | Comparing the bit distribution with other copies | | Software | Advanced developers | Test coding and agentic behavior locally | Trying a coding task with the model | | Multimodal work | Applied teams | Combine text and image inputs in one model | Asking a question about a picture | Skip it if you have a normal laptop or desktop, since the stored tensors take about 402 GiB. ### Where to use it - MLX based tools - Machines with hundreds of GB of memory - Windows, macOS and Linux, with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Reasoning and coding | Developers | Text replies from the full model | | Image and text input | Applied teams | Answers that use pictures | | Quantization study | Researchers | A reference mixed-precision checkpoint | ### How to set it up - Python, to run the download command - About 402 GiB of storage for the tensors, according to the card - MLX tooling that can load the checkpoint 1. Install the Hugging Face tools. 2. Download the model files to a drive with plenty of space. 3. Load the checkpoint with your MLX tooling. ```sh pip install -U huggingface_hub hf download mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp ``` ### How to use it The part of the card available here gives no run commands, only how the model was quantized. Load the checkpoint in MLX tooling that supports it, and see the original DeepSeek model for how to prompt it. | Component | Storage | | --- | --- | | Language backbone | 300.132 GB | | Engram tables | 122.884 GB | | Vision encoder | 0.971 GB | | MTP head | 8.047 GB | ### Example You load the checkpoint in an MLX tool on a machine with enough memory and ask a coding question. The model answers with text. The card does not show a sample run, so treat this as the general idea. ### For newcomers and experts This is a giant model squeezed into a smaller file, and it is meant for people with huge amounts of memory. If you are new, start with a small model first and come back to this one later. If you know the field, oQ4e leaves the projections at their FP4 and FP8 precision and shrinks only the Engram tables, using 4-bit affine storage in groups of 32. The base model uses compressed sparse attention, Engram conditional memory and DSpark speculative decoding. Without the Engram tables the average is 4.365 bits per weight. Start from DeepSeek-V4.1-Flash-1017 → Keep FP4 and FP8 projections → Quantize the Engram tables to 4-bit → Save the MLX checkpoint Storage by component, in GB | Label | Value | | --- | --- | | Language backbone | 300.132 GB | | Engram tables | 122.884 GB | | Vision encoder | 0.971 GB | | MTP head | 8.047 GB | ### Good to know - Size: 763.205B logical weights in total, with about 402.363 GiB of tensor storage. - Contents: it bundles the text model, the Engram lookup tables, a vision encoder, an aligner that joins vision to text, and the speculative decoding weights. - Source: a quantized copy of DeepSeek-V4.1-Flash-1017. - License: MIT, following the source model. - Cost to run: none named, though the memory needs are huge. - Data it reaches: it downloads model files from Hugging Face and works on the text and images you give it. - Not for: ordinary laptops or small desktops. ## README ````markdown # DeepSeek-V4.1-Flash-1017-oQ4e-mtp A mixed-precision MLX copy of DeepSeek-V4.1-Flash-1017, a huge multimodal mixture-of-experts model. Made for people with hundreds of GB of memory who want to run it with MLX tools. Made by mlx-community 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: This is a mixed-precision MLX version of DeepSeek-V4.1-Flash-1017 from deepseek-ai. The original is a small update that improves reasoning, coding, agent use and image understanding. It is a multimodal mixture of experts with 552B parameters in its backbone. The oQ method from oMLX version 0.7.1 quantized it, and the whole checkpoint averages 4.529 bits per weight. Who it suits: It suits advanced users and teams with huge amounts of memory who want to run a big multimodal model through MLX instead of a hosted service. ```table Industry | Who uses it | How they use it | Example AI infrastructure | Platform engineers | Host a huge open model on MLX hardware | Serving the model to internal tools Research | ML researchers | Study mixed-precision quantization of a huge model | Comparing the bit distribution with other copies Software | Advanced developers | Test coding and agentic behavior locally | Trying a coding task with the model Multimodal work | Applied teams | Combine text and image inputs in one model | Asking a question about a picture ``` Skip it if you have a normal laptop or desktop, since the stored tensors take about 402 GiB. Where to use it: - MLX based tools - Machines with hundreds of GB of memory - Windows, macOS and Linux, with Python, to download the files ```table Use case | Who | What they get back Reasoning and coding | Developers | Text replies from the full model Image and text input | Applied teams | Answers that use pictures Quantization study | Researchers | A reference mixed-precision checkpoint ``` How to set it up: - Python, to run the download command - About 402 GiB of storage for the tensors, according to the card - MLX tooling that can load the checkpoint 1. Install the Hugging Face tools. 2. Download the model files to a drive with plenty of space. 3. Load the checkpoint with your MLX tooling. ```sh pip install -U huggingface_hub hf download mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp ``` How to use it: The part of the card available here gives no run commands, only how the model was quantized. Load the checkpoint in MLX tooling that supports it, and see the original DeepSeek model for how to prompt it. ```table Component | Storage Language backbone | 300.132 GB Engram tables | 122.884 GB Vision encoder | 0.971 GB MTP head | 8.047 GB ``` Example: You load the checkpoint in an MLX tool on a machine with enough memory and ask a coding question. The model answers with text. The card does not show a sample run, so treat this as the general idea. For newcomers and experts: This is a giant model squeezed into a smaller file, and it is meant for people with huge amounts of memory. If you are new, start with a small model first and come back to this one later. If you know the field, oQ4e leaves the projections at their FP4 and FP8 precision and shrinks only the Engram tables, using 4-bit affine storage in groups of 32. The base model uses compressed sparse attention, Engram conditional memory and DSpark speculative decoding. Without the Engram tables the average is 4.365 bits per weight. ```flow Start from DeepSeek-V4.1-Flash-1017 Keep FP4 and FP8 projections Quantize the Engram tables to 4-bit Save the MLX checkpoint ``` ```chart Title: Storage by component, in GB Language backbone: 300.132 GB Engram tables: 122.884 GB Vision encoder: 0.971 GB MTP head: 8.047 GB ``` Good to know: - Size: 763.205B logical weights in total, with about 402.363 GiB of tensor storage. - Contents: it bundles the text model, the Engram lookup tables, a vision encoder, an aligner that joins vision to text, and the speculative decoding weights. - Source: a quantized copy of DeepSeek-V4.1-Flash-1017. - License: MIT, following the source model. - Cost to run: none named, though the memory needs are huge. - Data it reaches: it downloads model files from Hugging Face and works on the text and images you give it. - Not for: ordinary laptops or small desktops. ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: mlx - Parameters: 129B - Weights: about 516 GB in U32 - Formats: Safetensors - Downloads in the last 30 days: 8,491 - Likes: 21 - License: mit - Based on: deepseek-ai/DeepSeek-V4.1-Flash-1017 - Tags: safetensors, deepseek_v41, quantized, image-text-to-text ## Links - Model: https://huggingface.co/mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp - Files: https://huggingface.co/mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp ``` 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 [mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp on Hugging Face](https://huggingface.co/mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp) ## 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 | | --- | --- | --- | --- | --- | --- | | DeepSeek-V4.1-Flash-1017-oQ4e-mtp (this page) | 8,491 | 21 | 129B | mit | 2026-10-17 | | [DeepSeek-V4.1-Flash-NVFP4](https://mindket.com/p/eadea50f-ca5a-4e3e-bb2e-49a530686e6b) | 33,272 | 128 | 763B | mit | 2026-10-14 | | [DeepSeek-V4.1-Flash-1017-NVFP4](https://mindket.com/p/55f4d177-0a71-40fb-8807-e1d706971f4e) | 13,127 | 188 | 665B | mit | 2026-11-04 | | [DeepSeek-V4.1-Flash-1017 EXL3 EAQ v1 2.9 bpw](https://mindket.com/p/e2aa9049-7d40-46f6-b187-57f9ffaaccf1) | 8,113 | 46 | 105B | mit | 2026-10-20 | | [DeepSeek-V4.1-Flash-1017-oQ4e-ngram-mtp](https://mindket.com/p/22c8b38b-47a9-42c5-a1b3-a550e38b6c1a) | 3,238 | 13 | 289B | mit | 2026-10-22 | | [DeepSeek-V4.1-Flash-1017 EXL3 2.9 bpw](https://mindket.com/p/037efcc1-e5e0-465b-9448-56fd5a06fdc6) | 7,382 | 60 | 105B | mit | 2026-10-19 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## 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: 2026-10-17 - First published: 2026-10-17 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp on Hugging Face](https://huggingface.co/mlx-community/DeepSeek-V4.1-Flash-1017-oQ4e-mtp) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ad6fc24d-f834-4a49-9dd9-74cc54233154) - [Its page on Mindket](https://mindket.com/p/ad6fc24d-f834-4a49-9dd9-74cc54233154)