How much memory do I need to run ELYZA-japanese-Llama-2-13b-fast-instruct (Q4_K_M)?

The file size is 7.96 GB (measured). With a 4,096-token context, the estimated memory including the KV cache is 12.4 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.

What is the command to run ELYZA-japanese-Llama-2-13b-fast-instruct with Ollama?

Run "ollama run hf.co/mmnga/ELYZA-japanese-Llama-2-13b-fast-instruct-gguf:Q4_K_M" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.

mmnga

ELYZA-japanese-Llama-2-13b-fast-instruct

Parameters: 13.1B
Max context length: 4,096
Architecture: llama
License: Conditional

License: llama2

About ELYZA-japanese-Llama-2-13b-fast-instruct

ELYZA-japanese-Llama-2-13b-fast-instruct (mmnga) is a model published in GGUF format. GGUF files are published by mmnga/ELYZA-japanese-Llama-2-13b-fast-instruct-gguf. It has about 13.1B parameters, the llama architecture, a maximum context length of 4,096 tokens (values written in the GGUF).

There are 12 quantizations, with file sizes from 5.50 GB (Q2_K) to 14.0 GB (Q8_0) (actual sizes on Hugging Face).

According to mmnga/ELYZA-japanese-Llama-2-13b-fast-instruct-gguf, the license is Llama 2 Community License, which allows commercial use with conditions such as scale or purpose. 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

mmnga/ELYZA-japanese-Llama-2-13b-fast-instruct-gguf

Published by the original developer About this label

Last checked: Nov 11, 2026

Architecture: llama; Supported before llama.cpp b4409
Parameters: 13.1B
Max context length: 4,096
License: llama2 Conditional

File size and memory per quantization for ELYZA-japanese-Llama-2-13b-fast-instruct (mmnga/ELYZA-japanese-Llama-2-13b-fast-instruct-gguf)
Quantization	File size Measured	Quality guide	4,096 max
Q2_K	5.50 GB	Very large	9.93 GB
Q3_K_S	5.74 GB	Large	10.2 GB
Q3_K_M	6.42 GB	Medium	10.8 GB
Q3_K_L	7.01 GB	Medium	11.4 GB
Q4_0	7.46 GB	Medium	11.9 GB
Q4_K_S	7.50 GB	Medium	11.9 GB
Q4_K_M	7.96 GB	Small	12.4 GB
Q5_0	9.07 GB	Small	13.5 GB
Q5_K_S	9.07 GB	Small	13.5 GB
Q5_K_M	9.33 GB	Very small	13.8 GB
Q6_K	10.8 GB	Very small	15.2 GB
Q8_0	14.0 GB	Nearly identical	18.4 GB

Ollama  ollama run hf.co/mmnga/ELYZA-japanese-Llama-2-13b-fast-instruct-gguf:Q4_K_M

llama.cpp  llama-server -hf mmnga/ELYZA-japanese-Llama-2-13b-fast-instruct-gguf:Q4_K_M

License and commercial use

Llama 2 Community License Conditional

Allows commercial use. However, products or services with more than 700 million monthly active users need a separate license from Meta. You must follow the Acceptable Use Policy.

The original model's license has not been fetched yet, so this is based on the GGUF repository's label. 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 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/mmnga/ELYZA-japanese-Llama-2-13b-fast-instruct-gguf:Q4_K_M

llama.cpp

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

llama-server -hf mmnga/ELYZA-japanese-Llama-2-13b-fast-instruct-gguf:Q4_K_M

LM Studio

Paste mmnga/ELYZA-japanese-Llama-2-13b-fast-instruct-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 ELYZA-japanese-Llama-2-13b-fast-instruct (Q4_K_M)?: The file size is 7.96 GB (measured). With a 4,096-token context, the estimated memory including the KV cache is 12.4 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.
Can ELYZA-japanese-Llama-2-13b-fast-instruct be used commercially?: The license is Llama 2 Community License (Commercial use with conditions).Allows commercial use. However, products or services with more than 700 million monthly active users need a separate license from Meta. You must follow the Acceptable Use Policy. Check the original license text before deciding.
What is the command to run ELYZA-japanese-Llama-2-13b-fast-instruct with Ollama?: Run "ollama run hf.co/mmnga/ELYZA-japanese-Llama-2-13b-fast-instruct-gguf:Q4_K_M" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.