GLM-4.7-Flash: A Complete Guide to Local Deployment of the High-Performance 30B Mixture of Experts Model
In today’s AI landscape, large language models have become indispensable tools for developers and researchers. Among the latest innovations stands GLM-4.7-Flash—a remarkable 30 billion parameter Mixture of Experts (MoE) model designed specifically for local deployment. What makes this model truly stand out is its ability to deliver exceptional performance while requiring surprisingly modest hardware resources.
If you’ve been searching for a powerful AI model that can run entirely on your personal hardware without compromising on capabilities, GLM-4.7-Flash might be exactly what you need. This comprehensive guide will walk you through everything you need to know about this model—from fundamental concepts to advanced configuration techniques. Whether you’re new to local AI deployment or looking to maximize the potential of your existing setup, you’ll find practical, actionable information within these pages.
What Is GLM-4.7-Flash?
GLM-4.7-Flash is a 30B parameter Mixture of Experts (MoE) inference model developed by the Z.ai team, specifically optimized for local deployment. Despite its massive size, the model activates only approximately 3.6B parameters during inference, making it remarkably efficient while maintaining exceptional performance across multiple critical benchmarks—particularly in coding tasks, intelligent agent workflows, and conversational abilities.
One of the most impressive features of GLM-4.7-Flash is its support for context windows up to 200K tokens, enabling it to process extremely long documents and complex inputs. Even more remarkable is its hardware accessibility: the model can run with just 24GB of RAM/VRAM/unified memory (32GB for full precision), bringing high-end AI capabilities within reach of individual developers and small teams.
Benchmark Performance
GLM-4.7-Flash demonstrates exceptional results across multiple authoritative benchmark tests. Below is a comparison with competing models:
The data reveals GLM-4.7-Flash’s consistent leadership across most benchmarks, with particularly notable advantages in SWE-bench (software engineering benchmark) and τ²-Bench (reasoning capability test). This translates to superior performance when handling complex logical problems, programming tasks, and scenarios requiring deep analytical reasoning.
Local Deployment Options for GLM-4.7-Flash
GLM-4.7-Flash supports multiple inference frameworks, including vLLM, SGLang, and Hugging Face Transformers. Each option offers different advantages depending on your hardware configuration and use case requirements. Below, we provide detailed deployment instructions for each approach.
Deployment with Hugging Face Transformers
Transformers from Hugging Face offers the quickest path to getting started with GLM-4.7-Flash. Here’s a complete implementation example:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_PATH = "zai-org/GLM-4.7-Flash"
messages = [{ "role": "user", "content": "hello" }]
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
)
model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=MODEL_PATH,
torch_dtype=torch.bfloat16,
device_map="auto",
)
inputs = inputs.to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
output_text = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[1]:])
print(output_text)
Before using this approach, ensure you have the latest version of the transformers library installed:
pip install git+https://github.com/huggingface/transformers.git
Deployment with vLLM
vLLM is a high-performance inference and serving engine particularly well-suited for production environments. Here’s the command to serve GLM-4.7-Flash with vLLM:
vllm serve zai-org/GLM-4.7-Flash \
--tensor-parallel-size 4 \
--speculative-config.method mtp \
--speculative-config.num_speculative_tokens 1 \
--tool-call-parser glm47 \
--reasoning-parser glm45 \
--enable-auto-tool-choice \
--served-model-name glm-4.7-flash
To install vLLM with the necessary dependencies:
pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly
pip install git+https://github.com/huggingface/transformers.git
Deployment with SGLang
SGLang offers another efficient inference framework with excellent performance characteristics. The deployment command is as follows:
python3 -m sglang.launch_server \
--model-path zai-org/GLM-4.7-Flash \
--tp-size 4 \
--tool-call-parser glm47 \
--reasoning-parser glm45 \
--speculative-algorithm EAGLE \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--mem-fraction-static 0.8 \
--served-model-name glm-4.7-flash \
--host 0.0.0.0 \
--port 8000
Installing SGLang requires building from source, followed by updating the transformers library to the latest main branch.
GGUF Format Deployment with llama.cpp
For resource-constrained devices, the GGUF format provides the most flexibility. Here’s a step-by-step deployment guide:
-
First, obtain the latest version of llama.cpp:
apt-get update
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build \
-DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-imple llama-server llama-gguf-split
cp llama.cpp/build/bin/llama-* llama.cpp
-
Download the model (install required libraries first):
pip install huggingface_hub hf_transfer
import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
from huggingface_hub import snapshot_download
snapshot_download(
repo_id = "unsloth/GLM-4.7-Flash-GGUF",
local_dir = "unsloth/GLM-4.7-Flash-GGUF",
allow_patterns = ["*UD-Q4_K_XL*"],
)
-
Run the model for interactive conversation:
./llama.cpp/llama-cli \
--model unsloth/GLM-4.7-Flash-GGUF/GLM-4.7-Flash-UD-Q4_K_XL.gguf \
--threads -1 \
--ctx-size 16384 \
--fit on \
--seed 3407 \
--temp 0.2 \
--top-k 50 \
--top-p 0.95 \
--min-p 0.01 \
--dry-multiplier 1.1 \
--jinja
If you encounter high CPU usage or slow context processing with llama.cpp, try disabling flash attention:
--flash-attn off
Optimizing Output Quality: Reducing Repetition and Loops
A common challenge with large language models is the tendency to produce repetitive or circular outputs. GLM-4.7-Flash introduces an effective solution through the dry-multiplier parameter.
Recommended Parameter Settings
For most tasks, the following parameter combination delivers optimal results:
--temp 0.2 --top-k 50 --top-p 0.95 --min-p 0.01 --dry-multiplier 1.1
The --dry-multiplier 1.1 parameter is particularly crucial for reducing unnatural repetition, offering advantages over traditional repetition penalty approaches by being specifically optimized for GLM-4.7-Flash’s architecture.
Parameter Adjustments for Different Scenarios
Different use cases require tailored parameter configurations:
General Purpose Tasks:
-
temperature = 0.2 -
top_p = 0.95 -
min_p = 0.01 -
top-k = 50 -
dry-multiplier = 1.1
Tool Calling Scenarios:
-
temperature = 0.2 -
top_p = 0.95 -
min_p = 0.01 -
top-k = 50 -
dry-multiplier = 0.0 (or significantly reduced)
In tool calling contexts, reducing or disabling the dry-multiplier often produces better results as it allows the model greater freedom to generate structured outputs required for function calling.
When dry-multiplier Is Not Available
Some frameworks (like LM Studio) don’t support the dry-multiplier parameter. In these cases, disable repetition penalty and use at least 4-bit precision for best results:
-
temperature = 0.2 -
top_p = 0.95 -
min_p = 0.01 -
top-k = 50 -
Repeat Penalty = Disabled
If repetition issues persist, try the Z.ai team’s recommended alternative parameters:
-
General use: --temp 1.0 --top-p 0.95 -
Tool calling: --temp 0.7 --top-p 1.0
Tool Calling: Extending Model Capabilities
GLM-4.7-Flash supports sophisticated tool calling functionality, enabling your AI assistant to interact with external systems, perform calculations, call APIs, and execute code. This capability is essential for building practical, real-world applications.
Basic Tool Calling Example
The following example demonstrates a framework for enabling the model to perform mathematical calculations, execute Python code, and run system commands:
import json, subprocess, random
from typing import Any
def add_number(a: float | str, b: float | str) -> float:
return float(a) + float(b)
def multiply_number(a: float | str, b: float | str) -> float:
return float(a) * float(b)
def substract_number(a: float | str, b: float | str) -> float:
return float(a) - float(b)
def write_a_story() -> str:
return random.choice([
"A long time ago in a galaxy far far away... ",
"There were 2 friends who loved sloths and code... ",
"The world was ending because every sloth evolved to have superhuman intelligence... ",
"Unbeknownst to one friend, the other accidentally coded a program to evolve sloths... ",
])
def terminal(command: str) -> str:
if "rm " in command or "sudo " in command or "dd " in command or "chmod " in command:
msg = "Cannot execute 'rm, sudo, dd, chmod' commands since they are dangerous "
print(msg); return msg
print(f"Executing terminal command `{command}` ")
try:
return str(subprocess.run(command, capture_output = True, text = True, shell = True, check = True).stdout)
except subprocess.CalledProcessError as e:
return f"Command failed: {e.stderr} "
def python(code: str) -> str:
data = {}
exec(code, data)
del data["__builtins__"]
return str(data)
MAP_FN = {
"add_number": add_number,
"multiply_number": multiply_number,
"substract_number": substract_number,
"write_a_story": write_a_story,
"terminal": terminal,
"python": python,
}
After defining your tools, use this function to interact with the model:
from openai import OpenAI
def unsloth_inference(
messages,
temperature = 0.2,
top_p = 0.95,
top_k = 50,
min_p = 0.01,
repetition_penalty = 0.0,
):
messages = messages.copy()
openai_client = OpenAI(
base_url = "http://127.0.0.1:8001/v1",
api_key = "sk-no-key-required",
)
model_name = next(iter(openai_client.models.list())).id
print(f"Using model = {model_name}")
has_tool_calls = True
original_messages_len = len(messages)
while has_tool_calls:
print(f"Current messages = {messages}")
response = openai_client.chat.completions.create(
model = model_name,
messages = messages,
temperature = temperature,
top_p = top_p,
tools = tools if tools else None,
tool_choice = "auto" if tools else None,
extra_body = {
"top_k": top_k,
"min_p": min_p,
"dry_multiplier": repetition_penalty,
}
)
tool_calls = response.choices[0].message.tool_calls or []
content = response.choices[0].message.content or " "
tool_calls_dict = [tc.to_dict() for tc in tool_calls] if tool_calls else tool_calls
messages.append({
"role": "assistant",
"tool_calls": tool_calls_dict,
"content": content,
})
for tool_call in tool_calls:
fx, args, _id = tool_call.function.name, tool_call.function.arguments, tool_call.id
out = MAP_FN[fx](**json.loads(args))
messages.append(
{
"role": "tool",
"tool_call_id": _id,
"name": fx,
"content": str(out),
}
)
else:
has_tool_calls = False
return messages
Practical Application Examples
Mathematical Calculation Example:
messages = [{
"role": "user",
"content": [{"type": "text", "text": "What is today's date plus 3 days?"}],
}]
unsloth_inference(messages, temperature = 0.2, top_p = 0.95, top_k = -1, min_p = 0.01)
Python Code Execution Example:
messages = [{
"role": "user",
"content": [{"type": "text", "text": "Create a Fibonacci function in Python and find fib(20)."}],
}]
unsloth_inference(messages, temperature = 0.2, top_p = 0.95, top_k = -1, min_p = 0.01)
The tool calling capability dramatically expands GLM-4.7-Flash’s application scope, transforming it from a text generator into a versatile interface between AI and real-world systems.
Model Fine-Tuning: Customizing Your AI Assistant
While GLM-4.7-Flash delivers excellent out-of-the-box performance, fine-tuning for specific tasks or domains can further enhance its capabilities. Unsloth supports fine-tuning for GLM-4.7-Flash, but several important considerations apply:
-
Environment Requirements: transformers library v5 is required -
Hardware Demands: The 30B parameter model cannot run on free Colab GPUs; 16-bit LoRA fine-tuning requires approximately 60GB of VRAM -
MoE Architecture Considerations: By default, router layers are not fine-tuned, helping preserve the model’s reasoning capabilities
Fine-Tuning Strategies to Preserve Reasoning Abilities
To maintain the model’s reasoning capabilities after fine-tuning, consider these recommended strategies:
-
Mix direct answers with Chain-of-Thought examples in your training dataset -
Ensure at least 75% of training data includes reasoning processes, with 25% as direct answers -
Use at least 4-bit precision to maintain performance characteristics
Fine-tuning represents a powerful way to enhance model performance for specific tasks, but requires substantial computational resources and high-quality training data. For most users, optimizing prompt engineering with the pre-trained model may offer a more practical approach.
Production Deployment
When your application needs to serve multiple users or handle high concurrency requests, a more robust deployment solution becomes necessary. Using llama-server, you can deploy GLM-4.7-Flash as a production-grade service:
./llama.cpp/llama-server \
--model unsloth/GLM-4.7-Flash-GGUF/GLM-4.7-Flash-UD-Q4_K_XL.gguf \
--alias "unsloth/GLM-4.7-Flash" \
--threads -1 \
--fit on \
--seed 3407 \
--temp 0.2 \
--top-k 50 \
--top-p 0.95 \
--min-p 0.01 \
--dry-multiplier 1.1 \
--ctx-size 16384 \
--port 8001 \
--jinja
After deployment, interact with the model using the OpenAI-compatible API:
from openai import OpenAI
import json
openai_client = OpenAI(
base_url = "http://127.0.0.1:8001/v1",
api_key = "sk-no-key-required",
)
completion = openai_client.chat.completions.create(
model = "unsloth/GLM-4.7-Flash",
messages = [{"role": "user", "content": "What is 2+2?"}],
)
print(completion.choices[0].message.content)
This deployment approach supports tool calling, streaming responses, and other advanced features, making it suitable for integration into production systems.
Frequently Asked Questions
What hardware configuration is required for GLM-4.7-Flash?
GLM-4.7-Flash requires approximately 18GB of memory/VRAM when using 4-bit quantization, and 32GB for full precision mode. The 4-bit quantized version can run smoothly on most modern workstations or personal computers with high-end GPUs.
What’s the difference between dry-multiplier and repeat penalty?
The dry-multiplier is specifically designed for GLM-4.7-Flash to reduce unnatural repetition and looping, delivering better results than traditional repeat penalty approaches. They differ fundamentally in technical implementation and effectiveness, and cannot be used interchangeably.
How do I choose the right deployment method for my needs?
-
Quick testing/development: Use the Hugging Face transformers library -
Production services/high concurrency: Use vLLM or SGLang -
Resource-constrained devices: Use llama.cpp with GGUF format -
Applications requiring tool calling: Prioritize vLLM or SGLang, which offer more comprehensive tool calling support
Why is my model outputting repetitive content?
This is typically caused by improper parameter configuration. We recommend adding the --dry-multiplier 1.1 parameter, used in combination with --temp 0.2 --top-k 50 --top-p 0.95 --min-p 0.01. If your framework doesn’t support dry-multiplier, disable the repeat penalty function instead.
What should I do if tool calling isn’t working properly?
Tool calling requires proper parameter configuration. Try reducing the dry-multiplier value (toward 0.0) or disabling it completely. Additionally, ensure you’re using the correct tool-call-parser (glm47) and appropriate temperature settings (0.7 is recommended for tool calling).
Can I run GLM-4.7-Flash on consumer-grade graphics cards?
Yes, but you’ll need to use the 4-bit quantized version (such as UD-Q4_K_XL), and may require substantial system memory as supplementary resources. For GPUs with 24GB of VRAM (like the RTX 4090), the 4-bit quantized version will run smoothly.
Conclusion
GLM-4.7-Flash represents a significant milestone in locally deployable large language models—delivering exceptional performance while dramatically lowering hardware requirements. Whether you’re a developer, researcher, or AI enthusiast, this model provides a powerful and flexible tool that can transform how you work and create.
Throughout this guide, we’ve explored GLM-4.7-Flash’s capabilities, deployment methods, optimization techniques, and practical applications. When properly configured, this model can handle everything from simple conversations to complex reasoning tasks, and even interact with external systems through tool calling—truly becoming an indispensable assistant for your projects.
As AI technology continues to evolve, local deployment of powerful models will become increasingly accessible to individuals and small teams. This approach not only protects data privacy but also provides greater freedom for customization. GLM-4.7-Flash exemplifies this trend, enabling us to experience cutting-edge AI capabilities without relying on cloud services.
We hope this comprehensive guide helps you successfully deploy and utilize GLM-4.7-Flash in your projects. While technology moves quickly, deep understanding and hands-on practice remain the most reliable paths to mastering new tools. Now is the perfect time to open your terminal and experience the capabilities of this remarkable model firsthand.
