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DataPilot

sarashina2.2-3Bx8-moe

Parameters
16.6B
Max context length
8,192
Architecture
llama
License
Commercial OK
Base model
DataPilot/sarashina2.2-3Bx8-moe
License
mit

About sarashina2.2-3Bx8-moe

sarashina2.2-3Bx8-moe is a GGUF model based on DataPilot/sarashina2.2-3Bx8-moe on Hugging Face. GGUF files are published by mradermacher/sarashina2.2-3Bx8-moe-GGUF. It has about 16.6B parameters, the llama architecture, a maximum context length of 8,192 tokens (values written in the GGUF).

There are 11 quantizations, with file sizes from 6.12 GB (Q2_K) to 17.6 GB (Q8_0) (actual sizes on Hugging Face).

Running Q4_K_M (10.1 GB) with an 8,192-token context needs an estimated 12.2 GB of memory. For 8,192 tokens, a GPU with 12 GB or more of VRAM (such as GeForce RTX 3080 Ti, GeForce RTX 5070), or a Mac with 12 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, Q2_K (6.12 GB), needs an estimated 8.21 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

mradermacher/sarashina2.2-3Bx8-moe-GGUF

Converted and published by a third party About this label

Last checked: Nov 9, 2026

Architecture
llama
Supported before llama.cpp b4409
Parameters
16.6B
Max context length
8,192
License
mit Commercial OK
File size and memory per quantization for sarashina2.2-3Bx8-moe (mradermacher/sarashina2.2-3Bx8-moe-GGUF)
Quantization File size Measured Quality guide 4,096 8,192 max
Q2_K 6.12 GB Very large 7.54 GB 8.21 GB
Q3_K_S 7.23 GB Large 8.64 GB 9.32 GB
Q3_K_M 7.99 GB Medium 9.41 GB 10.1 GB
Q3_K_L 8.63 GB Medium 10.1 GB 10.7 GB
IQ4_XS 8.98 GB Small 10.4 GB 11.1 GB
Q4_K_S 9.47 GB Medium 10.9 GB 11.6 GB
Q4_K_M 10.1 GB Small 11.5 GB 12.2 GB
Q5_K_S 11.4 GB Small 12.8 GB 13.5 GB
Q5_K_M 11.8 GB Very small 13.2 GB 13.9 GB
Q6_K 13.6 GB Very small 15.0 GB 15.7 GB
Q8_0 17.6 GB Nearly identical 19.0 GB 19.7 GB 
Ollama  ollama run hf.co/mradermacher/sarashina2.2-3Bx8-moe-GGUF:Q4_K_M
llama.cpp  llama-server -hf mradermacher/sarashina2.2-3Bx8-moe-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

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/mradermacher/sarashina2.2-3Bx8-moe-GGUF:Q4_K_M

llama.cpp

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

llama-server -hf mradermacher/sarashina2.2-3Bx8-moe-GGUF:Q4_K_M

LM Studio

Paste mradermacher/sarashina2.2-3Bx8-moe-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 sarashina2.2-3Bx8-moe (Q4_K_M)?
The file size is 10.1 GB (measured). With a 8,192-token context, the estimated memory including the KV cache is 12.2 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.
Can sarashina2.2-3Bx8-moe 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 sarashina2.2-3Bx8-moe run in 8 GB, 12 GB, 16 GB or 24 GB of VRAM?
8 GB: up to Q2_K (est. 8.21 GB); 12 GB: up to Q4_K_M (est. 12.2 GB); 16 GB: up to Q6_K (est. 15.7 GB); 24 GB: up to Q8_0 (est. 19.7 GB) (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).
What is the command to run sarashina2.2-3Bx8-moe with Ollama?
Run "ollama run hf.co/mradermacher/sarashina2.2-3Bx8-moe-GGUF:Q4_K_M" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.