GLM-4.7-Flash

Deploy GLM-4.7-Flash (30B MoE) by Zhipu AI on Clore.ai — efficient language model with 59.2% SWE-bench performance

GLM-4.7-Flash is a 30-billion parameter Mixture-of-Experts language model by Zhipu AI that activates only 3B parameters per token. It delivers exceptional performance on coding and reasoning tasks, achieving 59.2% on SWE-bench while requiring only 10-12GB VRAM for FP16 inference. Released under the MIT license, it's an ideal choice for developers seeking frontier model quality at affordable single-GPU costs.

At a Glance

• Model Size: 30B total / 3B active parameters (MoE)

• License: MIT (fully commercial)

• Context: 128K tokens

• Performance: 59.2% SWE-bench, 75.4% HumanEval

• VRAM: ~10-12GB FP16, ~6GB INT8

• Speed: ~45-60 tok/s on RTX 4090

Why GLM-4.7-Flash?

Efficient Performance: GLM-4.7-Flash punches above its weight class. Despite using only 3B active parameters, it outperforms many 70B+ dense models on coding benchmarks. The MoE architecture provides 30B model quality at 7B model inference cost.

Single-GPU Friendly: Unlike massive models requiring multi-GPU setups, GLM-4.7-Flash runs comfortably on a single RTX 4090 or A100 40GB. This makes it perfect for development, fine-tuning, and cost-effective production deployments.

Coding Specialist: With 59.2% SWE-bench performance, GLM-4.7-Flash excels at software engineering tasks — code generation, debugging, refactoring, and technical documentation. It understands 20+ programming languages with deep context awareness.

MIT Licensed: No usage restrictions. Deploy commercially, fine-tune, or modify without licensing concerns. The complete weights and training recipes are freely available.

GPU Recommendations

GPU	VRAM	Performance	Daily Cost*
RTX 4090	24GB	~50 tok/s	~$2.10
RTX 3090	24GB	~35 tok/s	~$1.10
A100 40GB	40GB	~80 tok/s	~$3.50
A100 80GB	80GB	~90 tok/s	~$4.00
H100	80GB	~120 tok/s	~$6.00

Best Value: RTX 4090 offers the sweet spot of performance and cost for GLM-4.7-Flash.

*Estimated Clore.ai marketplace prices

Deploy with vLLM

Install vLLM

pip install vllm>=0.6.0
# or latest
pip install git+https://github.com/vllm-project/vllm.git

Single GPU Setup

vllm serve THUDM/glm-4-flash \
  --model THUDM/glm-4-flash \
  --tensor-parallel-size 1 \
  --dtype float16 \
  --max-model-len 32768 \
  --served-model-name glm-4.7-flash \
  --trust-remote-code

Query the Server

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1", 
    api_key="EMPTY"
)

response = client.chat.completions.create(
    model="glm-4.7-flash",
    messages=[
        {"role": "system", "content": "You are an expert Python developer."},
        {"role": "user", "content": "Write a FastAPI app with async SQLAlchemy and JWT auth"}
    ],
    max_tokens=2048,
    temperature=0.7
)

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

Deploy with SGLang

SGLang often provides better throughput for MoE models:

pip install "sglang[all]>=0.3.0"

# Launch server
python -m sglang.launch_server \
  --model-path THUDM/glm-4-flash \
  --port 30000 \
  --host 0.0.0.0 \
  --dtype float16 \
  --tp-size 1 \
  --context-length 32768

Deploy with Ollama

Simple setup for local development:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull model (will download ~18GB)
ollama pull glm4:7b-chat

# Run interactively
ollama run glm4:7b-chat

# API mode
ollama serve

Then query via REST API:

import requests

response = requests.post('http://localhost:11434/api/generate',
    json={
        'model': 'glm4:7b-chat',
        'prompt': 'Explain the MoE architecture in GLM-4.7-Flash',
        'stream': False
    }
)

print(response.json()['response'])

Docker Template

FROM nvidia/cuda:12.1-devel-ubuntu22.04

# Install Python 3.10
RUN apt-get update && apt-get install -y python3.10 python3-pip curl

# Install vLLM
RUN pip install vllm>=0.6.0 transformers

# Pre-download model (optional)
# RUN python3 -c "from transformers import AutoModel; AutoModel.from_pretrained('THUDM/glm-4-flash', trust_remote_code=True)"

EXPOSE 8000

CMD ["vllm", "serve", "THUDM/glm-4-flash", \
     "--host", "0.0.0.0", \
     "--port", "8000", \
     "--tensor-parallel-size", "1", \
     "--dtype", "float16", \
     "--trust-remote-code"]

Build and run:

docker build -t glm-4.7-flash .
docker run --gpus all -p 8000:8000 glm-4.7-flash

Code Generation Example

GLM-4.7-Flash excels at complex code generation:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="glm-4.7-flash",
    messages=[
        {"role": "user", 
         "content": """Create a Python class for a rate limiter with:
- Token bucket algorithm
- Async/await support  
- Redis backend
- Decorator for function rate limiting
- Proper error handling"""}
    ],
    max_tokens=2048,
    temperature=0.3
)

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

Tips for Clore.ai Users

• Memory Optimization: Use --dtype float16 to reduce VRAM usage. For 16GB GPUs, add --max-model-len 16384 to limit context.

• Batch Processing: Increase --max-num-seqs for higher throughput when serving multiple requests.

• Quantization: For RTX 3060/4060 (12GB), use AWQ or GPTQ quantized versions for ~6GB VRAM usage.

• Preemption: GLM-4.7-Flash handles interruptions gracefully — good for preemptible Clore.ai instances.

• Context Length: Default 128K context may be overkill. Set --max-model-len 32768 for most applications.

Troubleshooting

Issue	Solution
OutOfMemoryError	Reduce --max-model-len or use --dtype float16
Slow model loading	Pre-cache with huggingface-cli download THUDM/glm-4-flash
Import errors	Update transformers: pip install transformers>=4.40.0
Poor performance	Enable Flash Attention: pip install flash-attn
Connection refused	Check firewall: ufw allow 8000

Alternative Models

If GLM-4.7-Flash doesn't fit your needs:

• Qwen2.5-Coder-7B: Better pure coding, smaller footprint

• CodeQwen1.5-7B: Chinese + English coding specialist

• GLM-4-9B: Larger sibling with better reasoning

• DeepSeek-V3: 671B MoE for ultimate performance (multi-GPU)

Resources

• GLM-4-Flash on Hugging Face

• GLM-4 Technical Report

• vLLM Documentation

• SGLang GitHub

• Zhipu AI Platform