Instructions to use mudler/Qwen3.5-122B-A10B-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mudler/Qwen3.5-122B-A10B-APEX-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 mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16

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 mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16

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 mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16

Use Docker

docker model run hf.co/mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16

LM Studio
Jan
Ollama
How to use mudler/Qwen3.5-122B-A10B-APEX-GGUF with Ollama:
```
ollama run hf.co/mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
```
Unsloth Desktop

How to use mudler/Qwen3.5-122B-A10B-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mudler/Qwen3.5-122B-A10B-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16
```

Lemonade

How to use mudler/Qwen3.5-122B-A10B-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.5-122B-A10B-APEX-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use mudler/Qwen3.5-122B-A10B-APEX-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 mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16

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 mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mudler/Qwen3.5-122B-A10B-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16

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 "mudler/Qwen3.5-122B-A10B-APEX-GGUF:F16" \
  --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"

Qwen3.5-122B-A10B-APEX-GGUF / README.md

mudler

Banner: replace em dash with a comma

ad12e31 verified 28 days ago

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metadata

license: apache-2.0
base_model: Qwen/Qwen3.5-122B-A10B
tags:
  - gguf
  - quantized
  - apex
  - moe
  - mixture-of-experts
  - qwen3.5

⚡ Each donation = another big MoE quantized

I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant.
If APEX quants are useful to you, your support directly funds those bigger runs.

🎉 Patreon (Monthly) | ☕ Buy Me a Coffee | ⭐ GitHub Sponsors

💚 Big thanks to Hugging Face for generously donating additional storage, much appreciated.

Qwen3.5-122B-A10B APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Qwen3.5-122B-A10B.

Brought to you by the LocalAI team | APEX Project | Technical Report

Benchmark Results

All measurements on 8xRTX PRO 6000 Blackwell (768 GB VRAM). Perplexity on wikitext-2-raw, context 512. Accuracy benchmarks via llama.cpp (400 tasks each).

Configuration	Size (GB)	Perplexity	KL mean	HellaSwag	Winogrande	MMLU	ARC	tg128 (t/s)
Q8_0 (Unsloth)	121	4.819	0.004	85.5%	77.3%	44.19	57.19	85.5
Q5_K_S (Unsloth)	~81	4.826	0.007	85.3%	76.0%	43.80	57.86	90.4
UD-Q4_K_XL (Unsloth)	~72	4.829	0.010	84.8%	76.3%	44.25	55.85	91.8
APEX I-Balanced	83.4	4.831	0.008	85.5%	77.8%	43.86	57.86	96.7
APEX I-Quality	72.3	4.838	0.012	85.3%	77.3%	43.86	56.86	99.7
APEX Quality	72.3	4.848	0.013	85.5%	76.3%	44.44	55.52	99.8
APEX Balanced	83.4	4.840	0.008	85.0%	76.3%	43.93	56.86	96.7
APEX I-Compact	55.1	4.978	0.041	84.5%	77.5%	44.06	57.86	106.3
APEX Compact	55.1	5.046	0.049	84.5%	77.8%	43.54	56.19	106.2
APEX I-Mini	44.9	5.306	0.102	84.0%	75.3%	42.83	56.52	110.0

Highlights

APEX I-Balanced matches or beats Q8_0 on HellaSwag (85.5%), Winogrande (77.8% vs 77.3%), and ARC (57.86 vs 57.19) while being 31% smaller and 13% faster.
APEX I-Quality (72.3 GB) beats UD-Q4_K_XL at the same size on HellaSwag (85.3% vs 84.8%), Winogrande (77.3% vs 76.3%), and ARC (56.86 vs 55.85).
APEX I-Compact (55.1 GB) achieves 84.5% HellaSwag and 57.86 ARC at 55% less size than Q8_0 — fastest standard profile at 106 t/s.
APEX I-Mini (44.9 GB) is the smallest at 63% less size than Q8_0, still 84% HellaSwag, fastest at 110 t/s.
I-variants consistently improve over standard profiles across PPL, KL, and ARC.

Available Files

File	Profile	Size	Best For
Qwen3.5-122B-A10B-APEX-I-Balanced.gguf	I-Balanced	83.4 GB	Best overall -- matches Q8_0 quality at 31% less size
Qwen3.5-122B-A10B-APEX-I-Quality.gguf	I-Quality	72.3 GB	Best quality at ~72 GB tier
Qwen3.5-122B-A10B-APEX-Quality.gguf	Quality	72.3 GB	Highest MMLU (44.44)
Qwen3.5-122B-A10B-APEX-Balanced.gguf	Balanced	83.4 GB	General purpose, low KL
Qwen3.5-122B-A10B-APEX-I-Compact.gguf	I-Compact	55.1 GB	Consumer multi-GPU, best quality/size ratio
Qwen3.5-122B-A10B-APEX-Compact.gguf	Compact	55.1 GB	Consumer multi-GPU setups
Qwen3.5-122B-A10B-APEX-I-Mini.gguf	I-Mini	44.9 GB	Smallest viable, fastest inference

What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient -- edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

See the APEX project for full details, technical report, and scripts.

Architecture

Model: Qwen3.5-122B-A10B (Qwen3.5-MoE)
Layers: 48
Experts: 256 routed + 1 shared (8 active per token)
Total Parameters: 122B
Active Parameters: ~10B per token
APEX Config: 5+5 symmetric edge gradient across 48 layers

Run with LocalAI

local-ai run mudler/Qwen3.5-122B-A10B-APEX-GGUF@Qwen3.5-122B-A10B-APEX-I-Balanced.gguf

Credits

APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp.