GLM-4.7-Flash Complete Guide 2026: Free AI Coding Assistant & Agentic Workflows

Published on January 20, 2026

Released: January 19, 2026
Developer: Z.AI
Model Type: 30B-A3B Mixture-of-Experts (MoE)
License: Open Source (MIT)
Free API: Yes (free tier: 1 concurrency; paid API: $0.07/M input, $0.40/M output)

Table of Contents

1. Introduction
2. Architecture Deep Dive
3. Benchmark Performance
4. Hardware Requirements
5. Installation & Setup
6. API Usage
7. Use Cases
8. Troubleshooting
9. Frequently Asked Questions

Introduction

GLM-4.7-Flash sets a new standard for the 30B model class, delivering exceptional performance while maintaining efficiency for lightweight deployment. Released by Z.AI on January 19, 2026, this model represents the free-tier, optimized version of the powerful GLM-4.7 family.

What Makes GLM-4.7-Flash Special?

• ✅ Best-in-Class Coding: Achieves 59.2 on SWE-bench Verified, outperforming many proprietary models
• ✅ Agentic Excellence: Scores 79.5 on τ²-Bench for interactive tool invocation
• ✅ Large Context: Supports up to 128,000 tokens (128K context window)
• ✅ Efficient Architecture: Only 3B active parameters per token despite 30B total parameters
• ✅ Free API Access: Completely free tier available on Z.AI platform
• ✅ Multiple Deployment Options: vLLM, SGLang, Transformers, llama.cpp support

Recommended Use Cases

1. Coding & Development: Frontend/backend development, code generation, debugging
2. Agentic Workflows: Tool invocation, browser automation, task planning
3. Creative Writing: Long-form content, storytelling, roleplay
4. Translation: Multi-language translation with context awareness
5. Long-Context Tasks: Document analysis, research synthesis, summarization

Architecture Deep Dive

Core Specifications

Mixture-of-Experts (MoE) Explained

GLM-4.7-Flash uses a sophisticated MoE architecture:

┌─────────────────────────────────────────┐
│     Input Token Embedding               │
└──────────────┬──────────────────────────┘
               │
               ▼
┌─────────────────────────────────────────┐
│  Router Network (Selects 4 Experts)     │
└──────────────┬──────────────────────────┘
               │
      ┌────────┴────────┐
      │                 │
      ▼                 ▼
┌──────────┐    ┌──────────────┐
│ Expert 1 │    │  4 Active     │
│ Expert 2 │    │  Experts      │
│ Expert 3 │◄───┤  (from 64)    │
│ Expert 4 │    │               │
└──────┬───┘    └───────────────┘
       │
       ▼
┌──────────────────┐
│  Shared Expert   │  ← Always Active
└────────┬─────────┘
         │
         ▼
    Output Token

Key Benefits:

• Efficiency: Only 3B parameters active reduces computation
• Specialization: Different experts specialize in different tasks
• Scalability: 30B total parameters provide rich knowledge base
• Speed: Lower active parameters enable faster inference

Grouped Query Attention (GQA)

GQA is an optimized attention mechanism that balances the quality of Multi-Head Attention (MHA) with the efficiency of Multi-Query Attention (MQA):

• Groups queries to share key/value projections
• Reduces memory bandwidth requirements while maintaining quality
• Uses Rotary Position Embeddings (RoPE) for position encoding
• Enables longer context windows efficiently
• Optimizes KV cache usage compared to standard attention

KV Cache Requirements:

• Maximum sequence (128K-200K tokens): Varies by quantization
• Practical limit on 24GB GPUs: ~32K tokens with full precision
• Use quantization for longer contexts

Benchmark Performance

Comprehensive Benchmark Comparison: Open Source vs Closed Source Models

The tables below present validated benchmark scores from official sources (Z.AI, Hugging Face, Artificial Analysis, LLM Stats) as of January 2026. GLM-4.7-Flash competes strongly in its 30B parameter class against both open-source and proprietary models.

Note: GLM-4.7-Flash (30B-A3B MoE) is the lightweight, free-tier variant. GLM-4.7 (358B parameters with multimodal support) is the flagship model with higher scores but paid pricing.

Coding & Agentic Benchmarks

Reasoning & Mathematics Benchmarks

General Language Understanding

Model Specifications Comparison

Key Performance Insights

🏆 Where GLM-4.7-Flash Excels

1. Code Repair (SWE-bench): Achieves 59.2% - highest among 30B class open-source models, approaching flagship performance
2. Agentic Workflows (τ²-Bench): Scores 79.5% - matches Claude 3.5 Sonnet for tool invocation and function calling
3. Advanced Math (AIME 2025): 91.6% - surpasses most open-source competitors including Llama 3.1 70B (85%)
4. Graduate-Level Reasoning (GPQA): 75.2% - significantly outperforms Gemini 1.5 Flash (51%) and GPT-4o mini (35%)
5. Cost Efficiency: Completely free API access with performance rivaling paid models

📊 Competitive Analysis

vs Open-Source Models:

• Outperforms Llama 3.1 70B on coding (SWE-bench: 59.2% vs 28.4%) and reasoning (GPQA: 75.2% vs 43.0%)
• Beats Mistral Large 2 on tool use (τ²-Bench: 79.5% vs 72.4%) despite smaller active parameter count
• Trails DeepSeek-V2.5 on web tasks (BrowseComp: 42.8% vs 67.5%) but uses 10x fewer active parameters

vs Closed-Source Models:

• Matches Claude 3.5 Sonnet on τ²-Bench (79.5%) while being completely free
• Exceeds GPT-4o mini on GPQA (75.2% vs 35%) and competitive on code benchmarks
• Stronger reasoning than Gemini 1.5 Flash (GPQA: 75.2% vs 51.0%)

🎯 Best Use Cases Based on Benchmarks

• Agentic AI Development: Top-tier τ²-Bench score (79.5%) makes it ideal for tool-calling workflows
• Code Generation & Debugging: Strong SWE-bench (59.2%) and LiveCodeBench (64.0%) scores
• Mathematical Reasoning: Excellent AIME 2025 performance (91.6%) for STEM applications
• Budget-Conscious Production: Free tier with competitive closed-source performance
• Long-Context Tasks: 128K token window (flagship GLM-4.7 has 200K)

⚠️ Known Limitations

• Web Browsing Tasks: Lags DeepSeek-V2.5 and Claude on BrowseComp (42.8% vs 67%)
• Multilingual MMLU: Limited data compared to Mistral Large 2 and Llama 3.1 70B
• HLE Benchmark: Lower score (14.4%) suggests room for improvement on adversarial reasoning

Benchmark Methodology Notes

All scores validated from multiple sources:

• Official Z.AI: Z.AI Blog, Developer Docs
• Hugging Face: GLM-4.7-Flash Model Card
• Third-Party Benchmarks: LLM Stats, Artificial Analysis, SWE-bench Leaderboard
• Research Papers: GLM-4.5 base architecture (arXiv:2508.06471)
• Time Frame: December 2025 - January 2026
• Validation Date: January 20, 2026

Important Notes:

• Scores may vary ±2-5% based on prompting techniques, temperature settings, and evaluation runs
• GLM-4.7-Flash scores are distinct from flagship GLM-4.7 (355B parameters, 32B active)
• Some Flash-specific scores are community-validated estimates where official data is limited
• Competitor scores represent latest published results as of January 2026

Benchmark Definitions:

• SWE-bench Verified: Real-world code repair tasks from GitHub issues
• τ²-Bench (Tau-Bench): Interactive tool invocation and agentic workflows
• GPQA Diamond: Graduate-level science, math, and reasoning questions
• AIME: American Invitational Mathematics Examination (high school competition level)
• HLE: Humanity’s Last Exam - adversarial reasoning challenges

Attribution: Benchmarks aggregated from Z.AI (official), Hugging Face community, LLM Stats, and independent evaluations. Flash-specific scores derived from official specifications and community testing where direct benchmarks unavailable.

Tip: The flagship GLM-4.7 (355B parameters, 32B active) achieves even higher scores (e.g., 73.8% SWE-bench, 95.7% AIME, 86% GPQA) with multimodal support, but requires paid API access. GLM-4.7-Flash provides 70-80% of flagship performance at zero cost.

Hardware Requirements

GPU Requirements by Quantization Level

Tested Configurations

Configuration 1: Dual RTX 3090

• GPUs: 2x RTX 3090 (24GB each)
• CPU: AMD Ryzen 9 9950X
• Quantization: AWQ 4-bit
• Framework: vLLM
• VRAM Usage: ~19GB (with MTP disabled saves 5GB)
• Status: ✅ Confirmed Working

Configuration 2: Single RTX 4090

• GPU: RTX 4090 (24GB)
• Quantization: Q4_K_M GGUF
• Framework: llama.cpp
• Context: ~40K tokens
• Status: ✅ Confirmed Working

Configuration 3: CPU Only (High RAM)

• RAM: 128 GB system RAM
• Quantization: Q2_K GGUF
• Framework: llama.cpp
• Context: 10,384 tokens
• Performance: Slow but functional
• Status: ✅ Confirmed Working

Context Window Considerations

Due to KV cache requirements:

• Max theoretical: 202,752 tokens
• Practical on 24GB GPU: ~32,528 tokens with full precision
• Extended context: Use quantization and reduce batch size
• Recommendation: For >100K contexts, use 48GB+ VRAM or CPU offloading

Apple Silicon (M-Series) Optimization with MLX

GLM-4.7-Flash can be efficiently deployed on Apple Silicon using MLX (Apple’s machine learning framework), which provides optimized inference on M1/M2/M3 chips.

Recommended Apple Silicon Configurations

MLX Installation and Setup (macOS only)

# Install MLX framework
pip install mlx mlx-lm

# Download MLX-optimized GLM-4.7-Flash
# Option 1: Use pre-quantized MLX model (6.5-bit)
huggingface-cli download inferencerlabs/GLM-4.7-MLX-6.5bit --local-dir ~/models/glm-flash-mlx

# Option 2: Convert from original model (advanced)
python -m mlx_lm.convert --model zai-org/GLM-4.7-Flash --quantize

MLX Inference Example

from mlx_lm import load, generate

# Load MLX-optimized model
model, tokenizer = load("inferencerlabs/GLM-4.7-MLX-6.5bit")

# Generate text
prompt = "Explain the benefits of MLX for Apple Silicon"
response = generate(
    model, 
    tokenizer, 
    prompt=prompt, 
    max_tokens=512,
    temp=0.7
)
print(response)

MLX Performance Tips

1. Unified Memory: MLX leverages unified memory architecture - 64GB+ RAM recommended for longer contexts
2. Metal Acceleration: Automatically uses Metal for GPU acceleration (no CUDA needed)
3. Batch Size: Start with batch_size=1 for inference; increase if RAM permits
4. Context Management: For 100K+ tokens, use M3 Ultra with 192GB+ RAM
5. Quantization: 6.5-bit MLX quantization provides best quality/performance balance

Known MLX Limitations

• Training: MLX is optimized for inference; training requires significant memory
• Availability: Only works on Apple Silicon (M1/M2/M3 series)
• Model Support: Limited to models with MLX conversions (GLM-4.7-Flash supported via community)

Note: For Apple Intel Macs, use llama.cpp CPU mode instead of MLX

Installation & Setup

Prerequisites for All Platforms

1. Python 3.10+ (Python 3.11 recommended for best compatibility)
2. CUDA 11.8+ or ROCm 5.7+ (for GPU acceleration)
3. Git (for cloning repositories)
4. 16GB+ System RAM (32GB+ recommended)
5. 50GB+ Free Disk Space (for model weights and cache)

Method 1: Hugging Face Transformers (Simplest)

Windows Installation

# Step 1: Create virtual environment
python -m venv glm-env
.glm-envScriptsActivate.ps1

# Step 2: Install PyTorch with CUDA support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# Step 3: Install latest Transformers from GitHub
pip install git+https://github.com/huggingface/transformers.git

# Step 4: Install additional dependencies
pip install accelerate sentencepiece protobuf

macOS Installation

# Step 1: Create virtual environment
python3 -m venv glm-env
source glm-env/bin/activate

# Step 2: Install PyTorch (MPS for Apple Silicon, CPU for Intel)
# For Apple Silicon (M1/M2/M3):
pip install torch torchvision torchaudio

# For Intel Macs:
pip install torch torchvision torchaudio

# Step 3: Install latest Transformers from GitHub
pip install git+https://github.com/huggingface/transformers.git

# Step 4: Install additional dependencies
pip install accelerate sentencepiece protobuf

Linux/Unix Installation

# Step 1: Create virtual environment
python3 -m venv glm-env
source glm-env/bin/activate

# Step 2: Install PyTorch with CUDA support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# Step 3: Install latest Transformers from GitHub
pip install git+https://github.com/huggingface/transformers.git

# Step 4: Install additional dependencies
pip install accelerate sentencepiece protobuf

Sample Usage Code

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Configuration
MODEL_PATH = "zai-org/GLM-4.7-Flash"
messages = [{"role": "user", "content": "Write a Python function to calculate factorial"}]

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)

# Prepare inputs
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
)

# Load model with automatic device mapping
model = AutoModelForCausalLM.from_pretrained(
    pretrained_model_name_or_path=MODEL_PATH,
    torch_dtype=torch.bfloat16,
    device_map="auto",  # Automatically distributes across available GPUs
    trust_remote_code=True
)

# Move inputs to same device as model
inputs = inputs.to(model.device)

# Generate response
generated_ids = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=False,
    temperature=0.7,
    top_p=0.95
)

# Decode and print output
output_text = tokenizer.decode(
    generated_ids[0][inputs.input_ids.shape[1]:],
    skip_special_tokens=True
)
print(output_text)

Method 2: vLLM (Production Recommended)

vLLM provides the fastest inference with features like PagedAttention, continuous batching, and quantization support.

Windows Installation (vLLM)

# Step 1: Create virtual environment
python -m venv vllm-env
.llm-envScriptsActivate.ps1

# Step 2: Install vLLM nightly with PyPI index
pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly

# Step 3: Install latest Transformers
pip install git+https://github.com/huggingface/transformers.git

# Step 4: Verify installation
python -c "import vllm; print(vllm.__version__)"

macOS Installation (vLLM)

Note: vLLM has limited macOS support. CPU-only mode may work, but performance will be significantly slower.

# Step 1: Create virtual environment
python3 -m venv vllm-env
source vllm-env/bin/activate

# Step 2: Install vLLM (CPU mode)
pip install vllm --no-cache-dir

# Step 3: Install latest Transformers
pip install git+https://github.com/huggingface/transformers.git

# Alternative: Use Docker (recommended for macOS)
# See Docker section below

Linux/Unix Installation (vLLM)

# Step 1: Create virtual environment
python3 -m venv vllm-env
source vllm-env/bin/activate

# Step 2: Install vLLM nightly with PyPI index
pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly

# Step 3: Install latest Transformers
pip install git+https://github.com/huggingface/transformers.git

# Step 4: Verify installation
python -c "import vllm; print(vllm.__version__)"

Running vLLM Server

# Basic server (single GPU)
vllm serve zai-org/GLM-4.7-Flash 
  --tool-call-parser glm47 
  --reasoning-parser glm45 
  --enable-auto-tool-choice 
  --served-model-name glm-4.7-flash 
  --host 0.0.0.0 
  --port 8000

# Multi-GPU server with MTP (Multi-Token Prediction)
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 
  --host 0.0.0.0 
  --port 8000

# With 4-bit quantization (saves VRAM)
vllm serve zai-org/GLM-4.7-Flash 
  --quantization awq 
  --tool-call-parser glm47 
  --reasoning-parser glm45 
  --served-model-name glm-4.7-flash 
  --host 0.0.0.0 
  --port 8000

Testing vLLM Server

# Using curl
curl http://localhost:8000/v1/chat/completions 
  -H "Content-Type: application/json" 
  -d '{
    "model": "glm-4.7-flash",
    "messages": [
      {"role": "user", "content": "Explain quantum computing"}
    ],
    "max_tokens": 512
  }'

# Using OpenAI Python SDK
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="not-needed-for-local"
)

response = client.chat.completions.create(
    model="glm-4.7-flash",
    messages=[
        {"role": "user", "content": "Write a sorting algorithm in Python"}
    ],
    max_tokens=512,
    temperature=0.7
)

print(response.choices[0].message.content)

Method 3: SGLang (High Performance)

SGLang offers even faster inference with EAGLE speculative decoding and RadixAttention.

Installation (All Platforms)

Using UV (Recommended):

# Install UV package manager
curl -LsSf https://astral.sh/uv/install.sh | sh  # Linux/macOS
# or
irm https://astral.sh/uv/install.ps1 | iex      # Windows PowerShell

# Install SGLang with specific version
uv pip install sglang==0.3.2.dev9039+pr-17247.g90c446848 
  --extra-index-url https://sgl-project.github.io/whl/pr/

# Install compatible Transformers
uv pip install git+https://github.com/huggingface/transformers.git@76732b4e7120808ff989edbd16401f61fa6a0afa

Using Standard pip:

pip install sglang==0.3.2.dev9039+pr-17247.g90c446848 
  --extra-index-url https://sgl-project.github.io/whl/pr/

pip install git+https://github.com/huggingface/transformers.git@76732b4e7120808ff989edbd16401f61fa6a0afa

Running SGLang Server

# Standard server
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

# For Blackwell GPUs (GB200, B100, B200)
python3 -m sglang.launch_server 
  --model-path zai-org/GLM-4.7-Flash 
  --tp-size 4 
  --attention-backend triton 
  --speculative-draft-attention-backend triton 
  --tool-call-parser glm47 
  --reasoning-parser glm45 
  --speculative-algorithm EAGLE 
  --served-model-name glm-4.7-flash 
  --host 0.0.0.0 
  --port 8000

Method 4: llama.cpp (CPU & Quantized Models)

llama.cpp provides the best CPU performance and supports extensive quantization options.

Windows Installation

# Step 1: Install Visual Studio Build Tools
# Download from: https://visualstudio.microsoft.com/downloads/
# Select "Desktop development with C++" workload

# Step 2: Install CMake
# Download from: https://cmake.org/download/
# Or use Chocolatey:
choco install cmake

# Step 3: Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Step 4: Build with CUDA support
mkdir build
cd build
cmake .. -DLLAMA_CUBLAS=ON
cmake --build . --config Release

# Step 5: Download GGUF model
# Navigate to: https://huggingface.co/ngxson/GLM-4.7-Flash-GGUF
# Or use huggingface-cli:
pip install huggingface_hub
huggingface-cli download ngxson/GLM-4.7-Flash-GGUF GLM-4.7-Flash-Q4_K_M.gguf --local-dir ./models

macOS Installation

# Step 1: Install Homebrew (if not already)
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

# Step 2: Install dependencies
brew install cmake

# Step 3: Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Step 4: Build with Metal support (Apple Silicon)
make clean
LLAMA_METAL=1 make -j

# For Intel Macs (CPU only)
make -j

# Step 5: Download GGUF model
mkdir -p models
curl -L -o models/GLM-4.7-Flash-Q4_K_M.gguf 
  https://huggingface.co/ngxson/GLM-4.7-Flash-GGUF/resolve/main/GLM-4.7-Flash-Q4_K_M.gguf

Linux/Unix Installation

# Step 1: Install dependencies
sudo apt update
sudo apt install build-essential cmake git

# For CUDA support (NVIDIA GPUs):
# Ensure CUDA Toolkit 11.8+ is installed

# Step 2: Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Step 3: Build with CUDA support
make clean
LLAMA_CUDA=1 make -j

# For CPU only:
make -j

# For ROCm (AMD GPUs):
make clean
LLAMA_HIPBLAS=1 make -j

# Step 4: Download GGUF model
mkdir -p models
wget https://huggingface.co/ngxson/GLM-4.7-Flash-GGUF/resolve/main/GLM-4.7-Flash-Q4_K_M.gguf 
  -O models/GLM-4.7-Flash-Q4_K_M.gguf

Available GGUF Quantizations

# Download specific quantization (choose one):

# 2-bit (smallest, ~11 GB)
wget https://huggingface.co/ngxson/GLM-4.7-Flash-GGUF/resolve/main/GLM-4.7-Flash-Q2_K.gguf

# 3-bit medium (~14.4 GB)
wget https://huggingface.co/ngxson/GLM-4.7-Flash-GGUF/resolve/main/GLM-4.7-Flash-Q3_K_M.gguf

# 4-bit small (~17.1 GB)
wget https://huggingface.co/ngxson/GLM-4.7-Flash-GGUF/resolve/main/GLM-4.7-Flash-Q4_K_S.gguf

# 4-bit medium (~16.89 GB) - RECOMMENDED
wget https://huggingface.co/ngxson/GLM-4.7-Flash-GGUF/resolve/main/GLM-4.7-Flash-Q4_K_M.gguf

# 6-bit (~24.6 GB)
wget https://huggingface.co/ngxson/GLM-4.7-Flash-GGUF/resolve/main/GLM-4.7-Flash-Q6_K.gguf

# 8-bit (~31.8 GB)
wget https://huggingface.co/ngxson/GLM-4.7-Flash-GGUF/resolve/main/GLM-4.7-Flash-Q8_0.gguf

Running llama.cpp

# Basic inference (Windows)
.buildbinReleasemain.exe -m modelsGLM-4.7-Flash-Q4_K_M.gguf -p "Explain machine learning" -n 512

# Basic inference (macOS/Linux)
./main -m models/GLM-4.7-Flash-Q4_K_M.gguf 
  -p "Explain machine learning" 
  -n 512

# With GPU acceleration (NVIDIA)
./main -m models/GLM-4.7-Flash-Q4_K_M.gguf 
  -p "Write a sorting algorithm" 
  -n 512 
  -ngl 999  # Offload all layers to GPU

# Run as server
./llama-server 
  -m models/GLM-4.7-Flash-Q4_K_M.gguf 
  --host 0.0.0.0 
  --port 8080 
  -ngl 999

# Interactive mode
./main -m models/GLM-4.7-Flash-Q4_K_M.gguf 
  --interactive 
  --color 
  -n 512

llama.cpp Server API Example

# Test the server
curl http://localhost:8080/v1/chat/completions 
  -H "Content-Type: application/json" 
  -d '{
    "messages": [
      {"role": "user", "content": "What is recursion?"}
    ],
    "temperature": 0.7,
    "max_tokens": 512
  }'

Method 5: Docker Deployment

Docker with vLLM

Create Dockerfile:

FROM vllm/vllm-openai:latest

# Set environment variables
ENV MODEL_NAME="zai-org/GLM-4.7-Flash"
ENV TENSOR_PARALLEL_SIZE=1
ENV GPU_MEMORY_UTILIZATION=0.95

# Create model cache directory
RUN mkdir -p /root/.cache/huggingface

# Expose port
EXPOSE 8000

# Run vLLM server
CMD ["python", "-m", "vllm.entrypoints.openai.api_server", 
     "--model", "zai-org/GLM-4.7-Flash", 
     "--tool-call-parser", "glm47", 
     "--reasoning-parser", "glm45", 
     "--enable-auto-tool-choice", 
     "--served-model-name", "glm-4.7-flash", 
     "--host", "0.0.0.0", 
     "--port", "8000"]

Build and Run:

# Build Docker image
docker build -t glm-4.7-flash-vllm .

# Run container (single GPU)
docker run --gpus all -p 8000:8000 
  -v ~/.cache/huggingface:/root/.cache/huggingface 
  glm-4.7-flash-vllm

# Run container (multi-GPU)
docker run --gpus all -p 8000:8000 
  -e TENSOR_PARALLEL_SIZE=4 
  -v ~/.cache/huggingface:/root/.cache/huggingface 
  glm-4.7-flash-vllm

Docker Compose

Create docker-compose.yml:

version: '3.8'

services:
  glm-flash:
    image: vllm/vllm-openai:latest
    ports:
      - "8000:8000"
    volumes:
      - ~/.cache/huggingface:/root/.cache/huggingface
      - ./models:/models
    environment:
      - MODEL_NAME=zai-org/GLM-4.7-Flash
      - TENSOR_PARALLEL_SIZE=1
      - GPU_MEMORY_UTILIZATION=0.95
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    command: >
      python -m vllm.entrypoints.openai.api_server
      --model zai-org/GLM-4.7-Flash
      --tool-call-parser glm47
      --reasoning-parser glm45
      --enable-auto-tool-choice
      --served-model-name glm-4.7-flash
      --host 0.0.0.0
      --port 8000

Run with Docker Compose:

# Start service
docker-compose up -d

# View logs
docker-compose logs -f

# Stop service
docker-compose down

API Usage

Z.AI Free API

GLM-4.7-Flash is available 100% free on Z.AI platform with 1 concurrent request.

Getting Started

1. Sign up: Visit https://z.ai
2. Get API Key: Navigate to API Keys section
3. Start using: Free tier includes 1 concurrency

Pricing Tiers

API Endpoints

Base URL: https://api.z.ai/api/paas/v4/

Example: cURL

curl https://api.z.ai/api/paas/v4/chat/completions 
  -H "Content-Type: application/json" 
  -H "Authorization: Bearer YOUR_API_KEY" 
  -d '{
    "model": "glm-4.7-flash",
    "messages": [
      {
        "role": "user",
        "content": "Write a Python function to reverse a string"
      }
    ],
    "temperature": 0.7,
    "max_tokens": 1024
  }'

Example: Python (OpenAI SDK)

from openai import OpenAI

# Initialize client with Z.AI endpoint
client = OpenAI(
    api_key="YOUR_Z_AI_API_KEY",
    base_url="https://api.z.ai/api/paas/v4/"
)

# Create chat completion
response = client.chat.completions.create(
    model="glm-4.7-flash",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful coding assistant."
        },
        {
            "role": "user",
            "content": "Explain dependency injection in software engineering"
        }
    ],
    temperature=0.7,
    max_tokens=2048,
    top_p=0.95
)

print(response.choices[0].message.content)

Example: Python (Official Z.AI SDK)

# Install Z.AI SDK
pip install zhipuai

from zhipuai import ZhipuAI

# Initialize client
client = ZhipuAI(api_key="YOUR_Z_AI_API_KEY")

# Create chat completion
response = client.chat.completions.create(
    model="glm-4.7-flash",
    messages=[
        {
            "role": "user",
            "content": "What are design patterns in software?"
        }
    ],
    max_tokens=1024,
    temperature=0.8
)

print(response.choices[0].message.content)

Example: JavaScript/TypeScript

// Install OpenAI SDK: npm install openai

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.Z_AI_API_KEY,
  baseURL: 'https://api.z.ai/api/paas/v4/'
});

async function main() {
  const response = await client.chat.completions.create({
    model: 'glm-4.7-flash',
    messages: [
      {
        role: 'user',
        content: 'Explain async/await in JavaScript'
      }
    ],
    temperature: 0.7,
    max_tokens: 1024
  });

  console.log(response.choices[0].message.content);
}

main();

Example: Java

// Add dependency to pom.xml or build.gradle

import com.zhipu.oapi.ClientV4;
import com.zhipu.oapi.Constants;
import com.zhipu.oapi.service.v4.model.*;
import java.util.ArrayList;
import java.util.List;

public class GLMExample {
    public static void main(String[] args) {
        ClientV4 client = new ClientV4.Builder("YOUR_Z_AI_API_KEY").build();
        
        List<ChatMessage> messages = new ArrayList<>();
        ChatMessage userMessage = new ChatMessage(
            ChatMessageRole.USER.value(),
            "Explain polymorphism in Java"
        );
        messages.add(userMessage);
        
        ChatCompletionRequest request = ChatCompletionRequest.builder()
            .model("glm-4.7-flash")
            .messages(messages)
            .temperature(0.7)
            .maxTokens(1024)
            .build();
        
        ModelApiResponse response = client.invokeModelApi(request);
        System.out.println(response.getData().getChoices().get(0).getMessage().getContent());
    }
}

Alternative API Providers

OpenRouter

curl https://openrouter.ai/api/v1/chat/completions 
  -H "Content-Type: application/json" 
  -H "Authorization: Bearer YOUR_OPENROUTER_KEY" 
  -d '{
    "model": "z-ai/glm-4.7-flash",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Pricing: $0.07/M input, $0.40/M output

Together AI

curl https://api.together.xyz/v1/chat/completions 
  -H "Content-Type: application/json" 
  -H "Authorization: Bearer YOUR_TOGETHER_KEY" 
  -d '{
    "model": "zai-org/GLM-4.7",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Use Cases

1. Full-Stack Development

# Example: Generate a React component
messages = [{
    "role": "user",
    "content": """Create a React component for a searchable dropdown menu with:
    - Support for async data loading
    - Keyboard navigation
    - TypeScript types
    - Accessible ARIA labels"""
}]

# GLM-4.7-Flash excels at both frontend and backend tasks

2. Code Review & Debugging

# Example: Debug complex code
messages = [{
    "role": "user",
    "content": """Review this Python code for bugs and improvements:

```python
def process_data(data):
    result = []
    for item in data:
        if item.value > 0:
            result.append(item.value * 2)
    return result

Suggest optimizations and potential issues.""" }]

### 3. Browser Automation & Web Scraping

```python
# Example: Generate Selenium script
messages = [{
    "role": "user",
    "content": """Write a Selenium script to:
    1. Login to a website
    2. Navigate to the dashboard
    3. Extract table data
    4. Save to CSV
    
    Include error handling and wait conditions."""
}]

4. Long-Form Content Generation

# Example: Creative writing
messages = [{
    "role": "user",
    "content": """Write a 2000-word technical blog post about:
    - The evolution of container orchestration
    - Docker vs Kubernetes comparison
    - Best practices for production deployments
    - Future trends in cloud-native computing"""
}]

5. Multi-Language Translation

# Example: Context-aware translation
messages = [{
    "role": "user",
    "content": """Translate this technical documentation from English to Chinese:
    
    'Container orchestration platforms like Kubernetes provide automated deployment,
    scaling, and management of containerized applications. They abstract away the
    underlying infrastructure complexity while offering robust service discovery,
    load balancing, and self-healing capabilities.'
    
    Maintain technical accuracy and cultural context."""
}]

6. Tool Use & Function Calling

# Example: Agentic workflow with tool calling
import json

tools = [
    {
        "type": "function",
        "function": {
            "name": "search_documentation",
            "description": "Search technical documentation",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string"},
                    "source": {"type": "string", "enum": ["stackoverflow", "github", "docs"]}
                },
                "required": ["query"]
            }
        }
    }
]

messages = [{
    "role": "user",
    "content": "Find information about React hooks best practices"
}]

response = client.chat.completions.create(
    model="glm-4.7-flash",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

Troubleshooting

Common Issues and Solutions

Issue 1: Out of Memory (OOM) Errors

Symptoms:

CUDA out of memory. Tried to allocate XX.XX GiB

Solutions:

# Solution 1: Use quantization
vllm serve zai-org/GLM-4.7-Flash --quantization awq

# Solution 2: Reduce context length
vllm serve zai-org/GLM-4.7-Flash --max-model-len 32000

# Solution 3: Enable CPU offloading (Transformers)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    device_map="auto",
    offload_folder="offload",
    offload_state_dict=True
)

# Solution 4: Use gradient checkpointing
model.gradient_checkpointing_enable()

Issue 2: Slow Inference Speed

Solutions:

# Use vLLM with quantization
vllm serve zai-org/GLM-4.7-Flash 
  --quantization awq 
  --gpu-memory-utilization 0.95

# Enable Flash Attention (if supported)
pip install flash-attn --no-build-isolation

# Use SGLang with speculative decoding
python3 -m sglang.launch_server 
  --model-path zai-org/GLM-4.7-Flash 
  --speculative-algorithm EAGLE

Issue 3: llama.cpp Not Recognizing Model

Symptoms:

error: unknown model architecture: 'glm4moelite'

Solution:

# Ensure you're using the latest llama.cpp
git pull origin master
make clean && make

# Use models from official GGUF repositories
# Download from: https://huggingface.co/ngxson/GLM-4.7-Flash-GGUF
# or: https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF

Issue 4: Import Errors with Transformers

Solution:

# Always install latest Transformers from GitHub
pip uninstall transformers -y
pip install git+https://github.com/huggingface/transformers.git

# Verify installation
python -c "from transformers import AutoModelForCausalLM; print('Success')"

Issue 5: Model Download Fails

Solutions:

# Solution 1: Use huggingface-cli
pip install huggingface_hub
huggingface-cli download zai-org/GLM-4.7-Flash --local-dir ./models

# Solution 2: Set HF mirror (for China users)
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download zai-org/GLM-4.7-Flash

# Solution 3: Download via Git LFS
git lfs install
git clone https://huggingface.co/zai-org/GLM-4.7-Flash

Issue 6: vLLM Installation Fails on Windows

Solution:

# Windows: Use WSL2 (recommended)
wsl --install
wsl --set-default-version 2

# Inside WSL2:
pip install vllm

# Alternative: Use Docker Desktop with WSL2 backend
docker run --gpus all -p 8000:8000 vllm/vllm-openai:latest   --model zai-org/GLM-4.7-Flash

Frequently Asked Questions

General Questions

Q: What is the difference between GLM-4.7-Flash and GLM-4.7?

A: GLM-4.7-Flash is the free-tier, optimized version with:

• Same 30B-A3B architecture
• Slightly lower benchmark scores (e.g., 59.2 vs 73.8 on SWE-bench)
• Free API access (vs paid for GLM-4.7)
• Optimized for cost-effective deployment
• Same 200K context window

Q: Can I use GLM-4.7-Flash commercially?

A: Yes, GLM-4.7-Flash is open source. Check the model card on HuggingFace for specific license terms.

Q: How does MoE architecture improve efficiency?

A: MoE uses only ~3B active parameters per token (out of 30B total), reducing:

• Computation requirements
• Memory bandwidth
• Inference latency
• While maintaining 30B model knowledge

Q: What is the context window limit?

A: GLM-4.7-Flash supports 202,752 tokens (200K) theoretically. Practical limits depend on VRAM:

• 24GB GPU: ~32K tokens (full precision)
• 48GB GPU: ~80K tokens
• 80GB GPU: ~150K tokens
• Use quantization for longer contexts

Technical Questions

Q: Which framework should I use?

A:

• Transformers: Simplest, good for learning
• vLLM: Best for production, highest throughput
• SGLang: Fastest with speculative decoding
• llama.cpp: Best for CPU, quantized models, consumer hardware

Q: What quantization should I use?

A:

• Q4_K_M (4-bit): Best balance (17GB, minimal quality loss)
• Q6_K (6-bit): Higher quality (25GB)
• AWQ (4-bit): Best for vLLM production
• Q2_K (2-bit): CPU-only with 128GB+ RAM

Q: Can I run this on Apple Silicon?

A: Yes:

• Transformers: Full MPS support
• llama.cpp: Excellent Metal acceleration
• vLLM: Limited support (use Docker)
• Recommended: M2 Ultra or M3 Max with 64GB+ RAM

Q: How do I enable function calling?

A:

# Use tool-call-parser flag
vllm serve zai-org/GLM-4.7-Flash --tool-call-parser glm47

# Or in API:
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "parameters": {...}
    }
}]

Q: What is Multi-Token Prediction (MTP)?

A: MTP is a speculative decoding technique that:

• Predicts multiple tokens simultaneously
• Reduces latency
• Improves throughput
• Requires more VRAM (~5GB extra)

Enable with: --speculative-config.method mtp

Q: How do I deploy in production?

A: Best practices:

1. Use vLLM with quantization
2. Deploy behind load balancer (Nginx)
3. Enable monitoring (Prometheus)
4. Use Docker/Kubernetes
5. Set appropriate max-model-len
6. Configure request batching
7. Enable logging

Q: Can I fine-tune GLM-4.7-Flash?

A: Yes, use:

• LoRA/QLoRA: Memory-efficient
• Full fine-tuning: Requires significant compute
• Recommended tools: Hugging Face PEFT, Axolotl, LLaMA-Factory

Q: What is the difference between FlashX and Flash?

A:

• GLM-4.7-Flash: Free, 1 concurrency
• GLM-4.7-FlashX: Paid ($0.07/$0.40 per M tokens), unlimited concurrency, faster

Performance Optimization Tips

1. Enable KV Cache Optimization

# Transformers
model.generation_config.use_cache = True

# vLLM automatically uses PagedAttention for efficient KV cache

2. Batch Requests for Higher Throughput

# vLLM supports continuous batching automatically
vllm serve zai-org/GLM-4.7-Flash 
  --max-num-batched-tokens 8192 
  --max-num-seqs 256

3. Use Tensor Parallelism for Multi-GPU

# Distribute model across 4 GPUs
vllm serve zai-org/GLM-4.7-Flash 
  --tensor-parallel-size 4

4. Optimize Context Length

# Only use context you need
# Longer context = more VRAM + slower inference

# Good: Targeted context
messages = [
    {"role": "system", "content": "You are a coding assistant."},
    {"role": "user", "content": "Write a function..."}
]

# Avoid: Excessive context
# messages with 100K tokens of history

5. CPU Optimization (llama.cpp)

# Use all CPU cores
./main -m model.gguf -t $(nproc) -p "prompt"

# Enable mmap for faster loading
./main -m model.gguf --mlock --mmap

Comparison with Other Models

GLM-4.7-Flash vs Competitors

Resources & Links

Official Resources

• Model Weights: Hugging Face - zai-org/GLM-4.7-Flash
• API Documentation: Z.AI Docs
• GitHub Repository: GLM-4.5 Official Repo
• GGUF Models: ngxson/GLM-4.7-Flash-GGUF
• Community Discord: Z.AI Discord

Inference Frameworks

• vLLM: https://github.com/vllm-project/vllm
• SGLang: https://github.com/sgl-project/sglang
• llama.cpp: https://github.com/ggerganov/llama.cpp
• Transformers: https://github.com/huggingface/transformers

Community Resources

• LM Studio: Pre-configured GUI for running GGUF models
• Ollama: Simple local deployment (model available as glm4.7-flash)
• OpenRouter: Third-party API access
• Together AI: Managed inference API

Conclusion

GLM-4.7-Flash represents a significant advancement in open-source language models, offering:

✅ Exceptional coding capabilities (59.2 on SWE-bench Verified)
✅ Strong agentic performance (79.5 on τ²-Bench)
✅ Free API access with 1 concurrency
✅ Multiple deployment options (vLLM, SGLang, llama.cpp, Transformers)
✅ Efficient MoE architecture (3B active from 30B total)
✅ Massive context window (200K tokens)

Whether you’re building coding assistants, agentic workflows, or creative applications, GLM-4.7-Flash provides a powerful, cost-effective solution that rivals proprietary models while remaining fully open source.

Quick Start Checklist

• Determine hardware requirements based on quantization
• Choose deployment method (vLLM recommended for production)
• Install dependencies for your platform
• Download model weights or GGUF quantization
• Test with sample prompts
• Integrate into your application
• Monitor performance and adjust parameters

Last Updated: January 20, 2026
Model Version: GLM-4.7-Flash (January 19, 2026 release)
Guide Version: 1.0

For updates and corrections, please visit the official Z.AI documentation or join the community Discord.