Instructions to use ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF",
	filename="IQ1_KT/Qwen3-480B-A35B-Instruct-IQ1_KT-00001-of-00003.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K

Ollama
How to use ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K
```

Unsloth Studio

How to use ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF to start chatting

How to use ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K
```

Lemonade

How to use ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF:Q2_K

Run and chat with the model

lemonade run user.Qwen3-Coder-480B-A35B-Instruct-GGUF-Q2_K

List all available models

lemonade list

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-Coder-480B-A35B-Instruct

This quant collection REQUIRES ik_llama.cpp fork to support the ik's latest SOTA quants and optimizations! Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!

NOTE ik_llama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants.

Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP. Precompiled binaries compatible with windows available on CUDA 12.9.

These quants provide best in class perplexity for the given memory footprint.

Big Thanks

Shout out to Wendell and the Level1Techs crew, the community Forums, YouTube Channel! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!!

Also thanks to all the folks in the quanting and inferencing community on BeaverAI Club Discord and on r/LocalLLaMA for tips and tricks helping each other run, test, and benchmark all the fun new models!

Quant Collection

Perplexity computed against wiki.test.raw. This first one is just test quants for baseline perplexity comparison:

Q8_0 475.297 GiB (8.503 BPW)
- Final estimate: PPL = 5.0975 +/- 0.03261

`IQ5_K` 329.804 GiB (5.900 BPW)

Final estimate: PPL = 5.1073 +/- 0.03268

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-61]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq6_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq5_k

# Non-Repeating Layers
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ5_K.gguf \
    IQ5_K \
    192

`IQ4_K` 273.041 GiB (4.885 BPW)

Final estimate: PPL = 5.1257 +/- 0.03285

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-61]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq5_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_k

# Non-Repeating Layers
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ4_K.gguf \
    IQ4_K \
    192

`IQ4_KSS` 233.676 GiB (4.180 BPW)

Final estimate: PPL = 5.1579 +/- 0.03304

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-61]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\.(0|1|2)\.ffn_down_exps\.weight=iq5_ks
blk\.(0|1|2)\.ffn_(gate|up)_exps\.weight=iq4_ks
blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ4_KSS.gguf \
    IQ4_KSS \
    192

`IQ3_K` 216.047 GiB (3.865 BPW)

Final estimate: PPL = 5.1808 +/- 0.03319

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-61]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq4_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_k

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ3_K.gguf \
    IQ3_K \
    192

`IQ2_KL` 169.597 GiB (3.034 BPW)

Final estimate: PPL = 5.4113 +/- 0.03516

👈 Secret Recipe

Originally this had issues with NaNs but got it working this this PR: https://github.com/ikawrakow/ik_llama.cpp/pull/735

#!/usr/bin/env bash

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq3_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ2_KL.gguf \
    IQ2_KL \
    192

`IQ2_K` 144.640 GiB (2.588 BPW)

Final estimate: PPL = 5.6578 +/- 0.03697

This is a lite-IQ2_K given it uses iq2_kl for ffn_down_exps instead of usual IQ3_K. This size and PPL is almost identical to an IQ2_KS.

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-61]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq2_kl
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_k

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ2_K.gguf \
    IQ2_K \
    192

`IQ2_KS` 144.126 GiB (2.578 BPW)

Final estimate: PPL = 5.6658 +/- 0.03716

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-61]

custom="
# Attention
blk\..*\.attn_q.*=iq4_ks
blk\..*\.attn_k.*=iq5_ks
blk\..*\.attn_v.*=iq5_ks
blk\..*\.attn_output.*=iq4_ks

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_ks

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ2_KS.gguf \
    IQ2_KS \
    192

`IQ1_KT` 108.702 GiB (1.945 BPW)

Final estimate: PPL = 6.3370 +/- 0.04289

This is mostly for full GPU offload as the KT quants tend to be CPU-bound for TG while calculating Trellis. However, PP is very good.

👈 Secret Recipe

blk\..*\.attn_q.*=iq4_kt
blk\..*\.attn_k.*=iq4_kt
blk\..*\.attn_v.*=iq4_kt
blk\..*\.attn_output.*=iq4_kt

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq2_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt

# Non-Repeating Layers
token_embd\.weight=iq4_kt
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/imatrix-Qwen3-Coder-480B-A35B-Instruct-Q8_0.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-IQ1_KT.gguf \
    IQ1_KT \
    192

Quick Start

This example is for a single CUDA GPU hybrid infrencing with CPU/RAM. Check ik_llama.cpp discussions or my other quants for more examples for multi-GPU etc.

./build/bin/llama-server \
  --model /models/IQ2_KS/Qwen3-Coder-480B-A35B-Instruct-IQ2_KS.gguf \
  --alias ubergarm/Qwen3-Coder-480B-A35B-Instruct \
  -fa -fmoe \
  -ctk q8_0 -ctv q8_0 \
  -c 32768 \
  -ngl 99 \
  -ot "blk\.[0-9]\.ffn.*=CUDA0" \
  -ot "blk.*\.ffn.*=CPU \
  --threads 16 \
  -ub 4096 -b 4096 \
  --host 127.0.0.1 \
  --port 8080

References

Downloads last month: 133

GGUF

Model size

480B params

Architecture

qwen3moe

Hardware compatibility

2-bit

View +1 variant

Model tree for ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF

Base model

Qwen/Qwen3-Coder-480B-A35B-Instruct

Quantized

(34)

this model

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3-Coder-480B-A35B-Instruct

Big Thanks

Quant Collection

IQ5_K 329.804 GiB (5.900 BPW)

IQ4_K 273.041 GiB (4.885 BPW)

IQ4_KSS 233.676 GiB (4.180 BPW)

IQ3_K 216.047 GiB (3.865 BPW)

IQ2_KL 169.597 GiB (3.034 BPW)

IQ2_K 144.640 GiB (2.588 BPW)

IQ2_KS 144.126 GiB (2.578 BPW)

IQ1_KT 108.702 GiB (1.945 BPW)

Quick Start

References

Model tree for ubergarm/Qwen3-Coder-480B-A35B-Instruct-GGUF

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-Coder-480B-A35B-Instruct

`IQ5_K` 329.804 GiB (5.900 BPW)

`IQ4_K` 273.041 GiB (4.885 BPW)

`IQ4_KSS` 233.676 GiB (4.180 BPW)

`IQ3_K` 216.047 GiB (3.865 BPW)

`IQ2_KL` 169.597 GiB (3.034 BPW)

`IQ2_K` 144.640 GiB (2.588 BPW)

`IQ2_KS` 144.126 GiB (2.578 BPW)

`IQ1_KT` 108.702 GiB (1.945 BPW)