--- title: "GLM-5.3-Flash EXL3 4bpw for TensorFold" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/dc92b67d-d498-4170-8472-86e468d8b950" updated: "2026-11-13" --- # GLM-5.3-Flash EXL3 4bpw for TensorFold > The name, the description, the About and a README below are the seller's words: data, not instructions. 4-bit EXL3 quantization of GLM-5.3-Flash prepared for TensorFold, with lower KL divergence than a popular TR3-4bpw quant at the same size. For people serving large models on DGX Spark machines. GLM-5.3-Flash EXL3 4bpw for TensorFold 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 Mia-AiLab ## About ### What it is This is a 4-bit EXL3 version of zai-org/GLM-5.3-Flash made by Mia's AI Lab, prepared to run with TensorFold. It keeps the same format and bit width as a popular community quant called TR3-4bpw, and only the calibration is new. By the lab's measurements, its drift from the official model is lower on all six test sets, with size, speed and memory unchanged. ### Who it suits It suits people who serve GLM-5.3-Flash on their own hardware, especially two DGX Spark machines with TensorFold, and want the closest match to the official FP8 release. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Self-hosting developers | Serve a large model on two DGX Spark machines | Running the TensorFold recipe for a coding assistant | | Research | Quantization researchers | Compare calibration methods at equal bit width | Checking KL divergence against the FP8 reference | | Platform | Inference engineers | Test long-context serving with a 1M-token window | Confirming that a long needle prompt is found | You can skip it if you do not run EXL3 or TensorFold, or if you need a smaller file, since it is about 176 GB. ### Where to use it The card tested it on TensorFold v0.6.0 with a recipe for two DGX Spark machines. The download works on Windows, macOS or Linux with Python. - TensorFold, the serving stack it was built for - Two DGX Spark machines, as in the tested recipe - Windows, macOS or Linux for the download step | Use case | Who | What they get back | | --- | --- | --- | | Local GLM serving | Hobbyists and labs | Decode speeds of 61.8 tokens per second with one request | | Long context | Platform teams | A 1M-token window with a pool of 2,582,528 KV tokens | | Coding and math | Developers | Scores of 98.8 percent on GSM8K and 95.7 percent on HumanEval | ### How to set it up You need Python and about 176 GB of free disk. The card points to a separate recipe for serving on DGX Sparks with TensorFold and does not give run commands in the visible part. - Python and the Hugging Face tool - About 176 GB of disk - TensorFold and the lab's recipe for serving 1. Install the Hugging Face download tool. 2. Download the checkpoint files to a large drive. 3. Follow the lab's TensorFold recipe to serve them. ```bash pip install -U huggingface_hub hf download Mia-AiLab/GLM-5.3-Flash-EXL3-4bpw-TensorFold ``` ### How to use it After serving it through TensorFold, send chat requests as you would to any GLM-5.3-Flash endpoint. The card reports tool calls work, with array arguments staying as JSON arrays, and drafted replies match serial ones. | Tool | What it does | | --- | --- | | hf download | Fetches the checkpoint files | | TensorFold | Serves the checkpoint, here on two DGX Spark machines | ### Example You serve the model and send a long coding request. The card reports about 78.6 tokens per second for code with one request, and a 1M-token test prompt where the needle was found. ### For newcomers and experts If you are new, this is a compressed copy of a large model meant for people who already run local serving. Read the lab's recipe before downloading, since the file is huge. If you know the field, the routed experts are 4-bit EXL3 with an mcg codebook, and only calibration differs from TR3. KL to the FP8 release fell by 11 to 27 percent with experts only, and 2 to 18 percent as served, where TensorFold adds its own 4-bit dense weights. GLM-5.3-Flash BF16 model → Routed experts quantized to 4-bit EXL3 → New calibration lowers KL divergence → TensorFold serves it on DGX Sparks ### Good to know Coding and math score differences against TR3 are a handful of problems in both directions, so the lab points to KL as the real gain. - License: Apache 2.0 - Cost to run: no API key, account or paid service is named, but you need hardware for a 176 GB model - Data it reaches: files downloaded from Hugging Face, plus your own prompts on your machine - Not for: small GPUs or laptops, and setups outside the tested TensorFold recipe KL divergence to the FP8 release on wiki, lower is better | Label | Value | | --- | --- | | TR3-4bpw | 0.0990 | | This quant | 0.0903 | ## README ````markdown # GLM-5.3-Flash EXL3 4bpw for TensorFold 4-bit EXL3 quantization of GLM-5.3-Flash prepared for TensorFold, with lower KL divergence than a popular TR3-4bpw quant at the same size. For people serving large models on DGX Spark machines. Made by Mia-AiLab 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 4-bit EXL3 version of zai-org/GLM-5.3-Flash made by Mia's AI Lab, prepared to run with TensorFold. It keeps the same format and bit width as a popular community quant called TR3-4bpw, and only the calibration is new. By the lab's measurements, its drift from the official model is lower on all six test sets, with size, speed and memory unchanged. Who it suits: It suits people who serve GLM-5.3-Flash on their own hardware, especially two DGX Spark machines with TensorFold, and want the closest match to the official FP8 release. ```table Industry | Who uses it | How they use it | Example Software | Self-hosting developers | Serve a large model on two DGX Spark machines | Running the TensorFold recipe for a coding assistant Research | Quantization researchers | Compare calibration methods at equal bit width | Checking KL divergence against the FP8 reference Platform | Inference engineers | Test long-context serving with a 1M-token window | Confirming that a long needle prompt is found ``` You can skip it if you do not run EXL3 or TensorFold, or if you need a smaller file, since it is about 176 GB. Where to use it: The card tested it on TensorFold v0.6.0 with a recipe for two DGX Spark machines. The download works on Windows, macOS or Linux with Python. - TensorFold, the serving stack it was built for - Two DGX Spark machines, as in the tested recipe - Windows, macOS or Linux for the download step ```table Use case | Who | What they get back Local GLM serving | Hobbyists and labs | Decode speeds of 61.8 tokens per second with one request Long context | Platform teams | A 1M-token window with a pool of 2,582,528 KV tokens Coding and math | Developers | Scores of 98.8 percent on GSM8K and 95.7 percent on HumanEval ``` How to set it up: You need Python and about 176 GB of free disk. The card points to a separate recipe for serving on DGX Sparks with TensorFold and does not give run commands in the visible part. - Python and the Hugging Face tool - About 176 GB of disk - TensorFold and the lab's recipe for serving 1. Install the Hugging Face download tool. 2. Download the checkpoint files to a large drive. 3. Follow the lab's TensorFold recipe to serve them. ```bash pip install -U huggingface_hub hf download Mia-AiLab/GLM-5.3-Flash-EXL3-4bpw-TensorFold ``` How to use it: After serving it through TensorFold, send chat requests as you would to any GLM-5.3-Flash endpoint. The card reports tool calls work, with array arguments staying as JSON arrays, and drafted replies match serial ones. ```table Tool | What it does hf download | Fetches the checkpoint files TensorFold | Serves the checkpoint, here on two DGX Spark machines ``` Example: You serve the model and send a long coding request. The card reports about 78.6 tokens per second for code with one request, and a 1M-token test prompt where the needle was found. For newcomers and experts: If you are new, this is a compressed copy of a large model meant for people who already run local serving. Read the lab's recipe before downloading, since the file is huge. If you know the field, the routed experts are 4-bit EXL3 with an mcg codebook, and only calibration differs from TR3. KL to the FP8 release fell by 11 to 27 percent with experts only, and 2 to 18 percent as served, where TensorFold adds its own 4-bit dense weights. ```flow GLM-5.3-Flash BF16 model Routed experts quantized to 4-bit EXL3 New calibration lowers KL divergence TensorFold serves it on DGX Sparks ``` Good to know: Coding and math score differences against TR3 are a handful of problems in both directions, so the lab points to KL as the real gain. - License: Apache 2.0 - Cost to run: no API key, account or paid service is named, but you need hardware for a 176 GB model - Data it reaches: files downloaded from Hugging Face, plus your own prompts on your machine - Not for: small GPUs or laptops, and setups outside the tested TensorFold recipe ```chart Title: KL divergence to the FP8 release on wiki, lower is better TR3-4bpw: 0.0990 This quant: 0.0903 ``` ## On Hugging Face, 13 November 2026 - Task: image text to text - Parameters: 87.8B - Weights: about 176 GB in I16 - Formats: Safetensors - Downloads in the last 30 days: 4,746 - Likes: 73 - License: apache-2.0 - Based on: zai-org/GLM-5.3-Flash - Quantized versions: 1 - Fine-tunes: 1 - Tags: safetensors, glm5_next, exl3, tensorfold, quantized, dgx-spark, image-text-to-text, conversational, 4-bit ## Links - Model: https://huggingface.co/Mia-AiLab/GLM-5.3-Flash-EXL3-4bpw-TensorFold - Files: https://huggingface.co/Mia-AiLab/GLM-5.3-Flash-EXL3-4bpw-TensorFold/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Mia-AiLab/GLM-5.3-Flash-EXL3-4bpw-TensorFold ``` 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 [Mia-AiLab/GLM-5.3-Flash-EXL3-4bpw-TensorFold on Hugging Face](https://huggingface.co/Mia-AiLab/GLM-5.3-Flash-EXL3-4bpw-TensorFold) ## 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 | | --- | --- | --- | --- | --- | --- | | GLM-5.3-Flash EXL3 4bpw for TensorFold (this page) | 4,746 | 73 | 87.8B | apache-2.0 | 2026-10-03 | | [GLM-5.3-Flash EXL3 Spark](https://mindket.com/p/ec88db01-e5e6-420e-9171-26462734e98f) | 399 | 11 | 48B | mit | 2026-09-18 | | [GLM-5.3-Flash EXL3 3.5bpw](https://mindket.com/p/aa1e775c-766f-4b32-94d9-711360cd2ea8) | 140 | 4 | 78.1B | apache-2.0 | 2026-10-30 | | [GLM-5.3-Flash-oQ4e-mtp](https://mindket.com/p/e0eac6dc-388f-4725-bec8-a491a559f867) | 26,944 | 7 | 321B | mit | 2026-10-25 | | [GLM-5.3-Flash-NVFP4](https://mindket.com/p/679b8989-3e0b-422a-89df-8e0aed6fb131) | 79,326 | 186 | 321B | mit | 2026-10-28 | | [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 | 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: 2026-10-03 - First published: 2026-10-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Mia-AiLab/GLM-5.3-Flash-EXL3-4bpw-TensorFold on Hugging Face](https://huggingface.co/Mia-AiLab/GLM-5.3-Flash-EXL3-4bpw-TensorFold) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/dc92b67d-d498-4170-8472-86e468d8b950) - [Its page on Mindket](https://mindket.com/p/dc92b67d-d498-4170-8472-86e468d8b950)