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lapadatbogdan

GLM-5.3-Flash-Coder-RCO-152E

Parameters
169.6B
Max context length
1,048,576
Architecture
glm5-next
License
Commercial OK
Base model
zai-org/GLM-5.3-Flash
License
mit
llama.cpp
b11279+

About GLM-5.3-Flash-Coder-RCO-152E

GLM-5.3-Flash-Coder-RCO-152E is a GGUF model based on zai-org/GLM-5.3-Flash on Hugging Face. GGUF files are published by lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF. It has about 169.6B parameters, the glm5-next architecture, a maximum context length of 1,048,576 tokens (values written in the GGUF).

There are 4 quantizations, with file sizes from 47.4 GB (IQ2_XXS) to 180 GB (Q8_0) (actual sizes on Hugging Face).

Running Q4_K_M (103 GB) with an 8,192-token context needs an estimated 104 GB of memory, and an estimated 104 GB at 32,768 tokens. For 8,192 tokens, a GPU with 128 GB or more of VRAM, or a Mac with 128 GB or more of unified memory, is a rough guide (macOS caps how much memory the GPU can use, so leave some headroom). The smallest, IQ2_XXS (47.4 GB), needs an estimated 48.6 GB at 8,192 tokens.

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.

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).

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

lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF

Published by the original developer About this label

Last checked: Nov 11, 2026

Architecture
glm5-next
llama.cpp b11279 and later
Parameters
169.6B
Max context length
1,048,576
License
mit Commercial OK
File size and memory per quantization for GLM-5.3-Flash-Coder-RCO-152E (lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF)
Quantization File size Measured Quality guide 4,096 8,192 16,384 32,768 65,536 131,072 262,144 1,048,576 max
IQ2_XXS GLM-5.3-Flash-Coder-RCO-152E-IQ2_XXS 47.4 GB Very large 48.5 GB 48.6 GB 48.8 GB 49.1 GB 49.7 GB 51.0 GB 53.6 GB 69.1 GB
IQ2_XXS (2 parts) GLM-5.3-Flash-Coder-RCO-152E-IQ2_XXS-downIQ2_S 50.8 GB Very large 51.9 GB 52.0 GB 52.1 GB 52.4 GB 53.1 GB 54.4 GB 57.0 GB 72.5 GB
Q3_K_M (2 parts) 75.1 GB Medium 76.1 GB 76.2 GB 76.4 GB 76.7 GB 77.4 GB 78.7 GB 81.2 GB 96.7 GB
Q4_K_M (3 parts) 103 GB Small 104 GB 104 GB 104 GB 104 GB 105 GB 106 GB 109 GB 124 GB
Q8_0 (4 parts) 180 GB Nearly identical 181 GB 182 GB 182 GB 182 GB 183 GB 184 GB 187 GB 202 GB 
Ollama  ollama run hf.co/lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF:Q4_K_M
llama.cpp  llama-server -hf lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF:Q4_K_M

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

Mainline llama.cpp can load this model's format (glm5-next) from b11279 (Sep 30, 2026) onward. Ollama and LM Studio bundle llama.cpp internally, so an older app fails to load it with errors such as "unknown model architecture". If that happens, update the app.

Example commands using Q4_K_M. 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/lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF:Q4_K_M

llama.cpp

Pass the repository and quantization name to llama-server (or llama-cli) with -hf.

llama-server -hf lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF:Q4_K_M

LM Studio

Paste lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF into the search box, download it, and choose the quantization from the file list.

Frequently asked questions

How much memory do I need to run GLM-5.3-Flash-Coder-RCO-152E (Q4_K_M)?
The file size is 103 GB (measured). With a 8,192-token context, the estimated memory including the KV cache is 104 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.
Can GLM-5.3-Flash-Coder-RCO-152E 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-Coder-RCO-152E 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-Coder-RCO-152E with Ollama?
Run "ollama run hf.co/lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF:Q4_K_M" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.