peasantsmith
GLM-5.3-Flash-Maya
- Max context length
- 65,536
- Architecture
- llama
- License
- Commercial OK
- Base model
- zai-org/GLM-5.3-Flash
- License
- mit
About GLM-5.3-Flash-Maya
GLM-5.3-Flash-Maya is a GGUF model based on zai-org/GLM-5.3-Flash on Hugging Face. GGUF files are published by peasantsmith/GLM-5.3-Flash-Maya-GGUF. It has the llama architecture, a maximum context length of 65,536 tokens (values written in the GGUF).
There are 4 quantizations, with file sizes from 73.4 GB (IQ1_S) to 156 GB (IQ3_S) (actual sizes on Hugging Face).
Running IQ1_S (73.4 GB) with an 8,192-token context needs an estimated 74.5 GB of memory, and an estimated 74.8 GB at 32,768 tokens. For 8,192 tokens, a GPU with 96 GB or more of VRAM, or a Mac with 96 GB or more of unified memory, is a rough guide (macOS caps how much memory the GPU can use, so leave some headroom).
The original model is licensed under MIT License, which allows commercial use (keep the license text and copyright notice when redistributing). Check the original license for the terms of use. An mmproj file for image input is also available.
File size and estimated memory per quantization
- Up to 8 GB
- Up to 16 GB
- Up to 24 GB
- Up to 48 GB
- Up to 128 GB
- More
File sizes are the actual values on Hugging Face. Memory is estimated for each context length (KV cache f16). The formula is in How memory is calculated.
All memory figures are Estimated (KV cache f16). It should run on a setup with at least this much VRAM (or Mac unified memory). Click a value to see the devices and models that fit that amount (Find by GPU / Mac).
Using image input: This model supports image input. To use images, an mmproj file is loaded as well.
About the "quality guide" (how far a quantization drifts from the original)
For each quantization type, how far the output drifts from the original model, grouped into six levels from values llama.cpp measured on Llama-3-8B.
The value is mean KLD (the difference between the original and quantized model in next-token probabilities); smaller means closer to the original. Quantizations made with an imatrix (importance data) come out slightly smaller. Levels use the value without an imatrix (or with one, for types that only have that).
These values were not measured on this model. Larger models and MoE models tend to drift less with the same type. Distributor-specific mixed types (such as UD-Q4_K_XL) cannot be matched by type name alone, so they show "-".
| Quantization | Quality guide | KLD (no imatrix) | KLD (imatrix) | Size (Llama-3-8B) |
|---|---|---|---|---|
| Q8_0 | Nearly identical | 0.0014 | - | 7.96 GiB |
| Q6_K | Very small | 0.0055 | - | 6.14 GiB |
| Q5_K_M | Very small | 0.0108 | - | 5.33 GiB |
| Q5_K_S | Small | 0.0166 | - | 5.21 GiB |
| Q5_1 | Small | 0.0180 | - | 5.65 GiB |
| Q5_0 | Small | 0.0222 | - | 5.21 GiB |
| Q4_K_M | Small | 0.0313 | 0.0282 | 4.58 GiB |
| Q4_K_S | Medium | 0.0431 | 0.0320 | 4.37 GiB |
| IQ4_NL | Small | - | 0.0357 | 4.35 GiB |
| IQ4_XS | Small | - | 0.0363 | 4.14 GiB |
| Q4_1 | Medium | 0.0717 | - | 4.78 GiB |
| Q4_0 | Medium | 0.0719 | - | 4.34 GiB |
| Q3_K_L | Medium | 0.0872 | 0.0731 | 4.03 GiB |
| Q3_K_M | Medium | 0.1019 | 0.0844 | 3.74 GiB |
| IQ3_M | Medium | - | 0.1025 | 3.53 GiB |
| IQ3_S | Medium | - | 0.1113 | 3.42 GiB |
| IQ3_XS | Large | - | 0.1387 | 3.28 GiB |
| IQ3_XXS | Large | - | 0.1836 | 3.05 GiB |
| Q3_K_S | Large | 0.2282 | 0.1998 | 3.41 GiB |
| IQ2_M | Large | - | 0.3260 | 2.74 GiB |
| Q2_K | Very large | 0.4451 | 0.3322 | 2.96 GiB |
| Q2_K_S | Very large | - | 0.4034 | 2.96 GiB |
| IQ2_S | Very large | - | 0.4392 | 2.56 GiB |
| IQ2_XS | Very large | - | 0.5463 | 2.43 GiB |
| IQ2_XXS | Very large | - | 0.8120 | 2.24 GiB |
| IQ1_M | Very large | - | 1.3931 | 2.01 GiB |
| IQ1_S | Very large | - | 2.2546 | 1.88 GiB |
Source: llama.cpp (MIT), measured on Llama-3-8B against FP16, Wikitext-2, revision f364eb6f. Original table · Reading and choosing quantizations
GGUF source
peasantsmith/GLM-5.3-Flash-Maya-GGUF
Published by the original developer About this label
Last checked: Nov 11, 2026
- Architecture
- llama
- Supported before llama.cpp b4409
- Max context length
- 65,536
- License
- mit Commercial OK
| Quantization | File size Measured | Quality guide | 4,096 | 8,192 | 16,384 | 32,768 | 65,536 max |
|---|---|---|---|---|---|---|---|
| IQ1_S (2 parts) | 73.4 GB | Very large | 74.5 GB | 74.5 GB | 74.6 GB | 74.8 GB | 75.2 GB |
| IQ2_XXS (3 parts) Maya-S24 | 94.7 GB | Very large | 95.8 GB | 95.8 GB | 95.9 GB | 96.1 GB | 96.4 GB |
| IQ2_XXS (3 parts) Maya-S-v2-IQ2_XXS | 96.5 GB | Very large | 97.5 GB | 97.6 GB | 97.7 GB | 97.8 GB | 98.2 GB |
| IQ2_S (3 parts) | 116 GB | Very large | 117 GB | 117 GB | 117 GB | 117 GB | 118 GB |
| IQ3_S (4 parts) | 156 GB | Medium | 157 GB | 157 GB | 158 GB | 158 GB | 158 GB |
The mmproj file for image input (F16) is 1.13 GB Measured . It is not included in the memory above, so add it if you use images.
Ollama ollama run hf.co/peasantsmith/GLM-5.3-Flash-Maya-GGUF:IQ1_S
llama.cpp llama-server -hf peasantsmith/GLM-5.3-Flash-Maya-GGUF:IQ1_S
License and commercial use
MIT License Commercial OK
Allows commercial use, modification and redistribution. When redistributing, keep the copyright notice and license text.
Based on the license on the original model's Hugging Face page. Original license text (checked Oct 18, 2026)
The summary states facts this site read from the original text and is not legal advice. Always check the original license and its latest terms before use. All licenses
How to run
Example commands using IQ1_S. To use another quantization, replace the name with one from the table above. How to run local LLMs
Ollama
Run it by pointing directly at the GGUF on Hugging Face.
ollama run hf.co/peasantsmith/GLM-5.3-Flash-Maya-GGUF:IQ1_S
llama.cpp
Pass the repository and quantization name to llama-server (or llama-cli) with -hf.
llama-server -hf peasantsmith/GLM-5.3-Flash-Maya-GGUF:IQ1_S
LM Studio
Paste peasantsmith/GLM-5.3-Flash-Maya-GGUF into the search box, download it, and choose the quantization from the file list.
Other models from peasantsmith
Frequently asked questions
- How much memory do I need to run GLM-5.3-Flash-Maya (IQ1_S)?
- The file size is 73.4 GB (measured). With a 8,192-token context, the estimated memory including the KV cache is 74.5 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.
- Can GLM-5.3-Flash-Maya be used commercially?
- The original model's license is MIT License (Commercial use allowed).Allows commercial use, modification and redistribution. When redistributing, keep the copyright notice and license text. Check the original license text before deciding.
- Does GLM-5.3-Flash-Maya run in 8 GB, 12 GB, 16 GB or 24 GB of VRAM?
- 8 GB: no quantization fits; 12 GB: no quantization fits; 16 GB: no quantization fits; 24 GB: no quantization fits (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).
- What is the command to run GLM-5.3-Flash-Maya with Ollama?
- Run "ollama run hf.co/peasantsmith/GLM-5.3-Flash-Maya-GGUF:IQ1_S" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.