Instructions to use ubergarm/GLM-4.7-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 ubergarm/GLM-4.7-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/GLM-4.7-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/GLM-4.7-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/GLM-4.7-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/GLM-4.7-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/GLM-4.7-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/GLM-4.7-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/GLM-4.7-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/GLM-4.7-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/GLM-4.7-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/GLM-4.7-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/GLM-4.7-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/GLM-4.7-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/GLM-4.7-GGUF:Q2_K

Ollama
How to use ubergarm/GLM-4.7-GGUF with Ollama:
```
ollama run hf.co/ubergarm/GLM-4.7-GGUF:Q2_K
```
Unsloth Desktop

How to use ubergarm/GLM-4.7-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/GLM-4.7-GGUF:Q2_K

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": "ubergarm/GLM-4.7-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ubergarm/GLM-4.7-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/GLM-4.7-GGUF:Q2_K
```

Lemonade

How to use ubergarm/GLM-4.7-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/GLM-4.7-GGUF:Q2_K

Run and chat with the model

lemonade run user.GLM-4.7-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use ubergarm/GLM-4.7-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/GLM-4.7-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/GLM-4.7-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ubergarm/GLM-4.7-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/GLM-4.7-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/GLM-4.7-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"

anyone running via cpu+gpu+rpc gpu ?

by gopi87 - opened Dec 30, 2025

Discussion

gopi87

Dec 30, 2025

i am getting very slow when i am running like this lol any help

ubergarm

Owner Dec 31, 2025

@gopi87

Heya gopi! I'd have to see your full command to help out better and understand your setup too especially given you are using RPC.

RPC is not the most supported feature, and not likely to give best performance as you probably know.

Your best bet is trying to run on a single system with CPU+GPU(s).

Maybe a smaller quant would fit on a single rig? Otherwise, you'll have to play some games and do some research to figure out the best way of organizing the order of devices for using RPC. Also I don't think RPC can take advantage of the new -sm parallel last time I tried, but things move so quickly who knows today haha..

Happy new year!

gopi87

Dec 31, 2025

happy new year!

ubergarm i have 256 ram and dual cpu and 12gb rtx and extranal connected gpu 3080 with 16vram.
looks like i need to add another gou in my server for boost up .

CUDA_VISIBLE_DEVICES="" ./bin/llama-server
--model "/home/gopi/deepresearch-ui/model/MiMo-V2-Flash-Q4_K_M-00001-of-00004.gguf"
--ctx-size 30000
--threads 40
--threads-batch 40
--host 0.0.0.0
--jinja
--port 8080
--mlock
--no-mmap

CUDA_VISIBLE_DEVICES="0" ./bin/llama-server
--model "/home/gopi/deepresearch-ui/model/MiniMax-M2.1-MXFP4_MOE-00001-of-00007.gguf"
--ctx-size 20000
-ngl 99
--n-cpu-moe 63
--threads 28
--threads-batch 28
--host 0.0.0.0
--mlock
--no-mmap
--jinja
--port 8080

i am currently expolering this two model and also i am thinking of to create a website for people share there model tricks and system specification so that it would be help to everyone. whats your on thoughts this ?

ubergarm

Owner Jan 4

ubergarm i have 256 ram and dual cpu and 12gb rtx and extranal connected gpu 3080 with 16vram.
looks like i need to add another gou in my server for boost up .

Dual CPU can be tricky depending on how you have configured NUMA nodes. You might want to try using something like when you need all the RAM:

numactl --interleave=all llama-server --numa distribute ...

If your quant can fit in a single NUMA node, consider something like this:

numactl -N 0 -m 0 llama-server --numa numactl ...

MXFP4

I haven't tried MiniMax but looking at the modelcard https://huggingface.co/MiniMaxAI/MiniMax-M2.1 it does not seem to be released in MXFP4 so I would avoid that format unless the original release specifies that is the correct quant type to use. I would prefer usually ik's IQ4_KSS at 4.0 BPW or similar newer types if you can find or quantize them. I haven't done this model assuming it is supported on ik. Otherwise Q4_K_M is probably pretty good choice.

i am thinking of to create a website for people share there model tricks and system specification so that it would be help to everyone. whats your on thoughts this ?

There are so many features in ik_llama.cpp something like this could be useful. Though it is difficult to keep it up to date as it changes so quickly. Usually people can track the ik_llama.cpp PR history for the latest information, or sometimes read my most recent discussions on huggingface to get some tips. Or hang out on the Beaver AI discord for real-time chat.

But definitely share the link if you give it a go! Cheers!

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