GLM-4-32B-0414.w4a16-gptq
GLM-4-32B-0414.w4a16-gptq is a 4-bit quantized version of the GLM-4 32B language model, designed to run on consumer-grade GPUs (32GB+ VRAM). It trades some accuracy for lower memory footprint and faster inference. The MIT license permits commercial use. Last updated November 2025.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | mratsim |
| Parameters | 33B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 60.9k |
| Likes | 3 |
| Last updated | 2025-11-08 |
| Source | mratsim/GLM-4-32B-0414.w4a16-gptq |
What GLM-4-32B-0414.w4a16-gptq is
A GPTQ-quantized (asymmetric, 4-bit weight, 16-bit activations) derivative of GLM-4-32B, calibrated on 2048 Pile validation samples at 4096 sequence length using llmcompressor. Supports vLLM, TensorRT-LLM, and text-generation-inference. Rope scaling configured for ~130k context. Model card states this is the uploader's first quantization; calibration choices were pragmatic rather than exhaustively validated.
Run GLM-4-32B-0414.w4a16-gptq locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="mratsim/GLM-4-32B-0414.w4a16-gptq")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Minimum 32GB VRAM (single GPU). Model card example uses NVIDIA GPU with vLLM at 90% memory utilization. Estimated ~16–18 GB for model weights (4-bit) + ~8–10 GB for activations/KV cache; precise VRAM depends on batch size and context length. No CPU-only or low-end GPU validation provided. Calibrated for max sequence length 4096; rope scaling extends to ~130k at potential quality cost.
Unknown. No fine-tuning guidance or LoRA/QLoRA compatibility stated in the model card. Quantized models can be challenging to fine-tune; recommend testing on a small validation set before committing. Consider the base model (zai-org/GLM-4-32B-0414) if full-precision fine-tuning is planned.
When to avoid it — and what to weigh
- Strict accuracy/quality requirements without validation — 4-bit quantization reduces precision. If your application demands high reasoning accuracy or low perplexity, benchmark against the full-precision model first. No comparative benchmarks are provided in the card.
- Enterprise production without governance review — Model is quantized by a community contributor with limited validation history (60k downloads, 3 likes). Requires your team's review of quantization fidelity and potential drift before production deployment.
- Latency-critical / real-time constraints — Consumer GPU serving introduces variable latency. If you need sub-100ms inference guarantees, consider specialized hardware (TPUs, optimized inference stacks) or smaller models.
- Multi-GPU or high-throughput batch serving — Optimized for single 32GB GPU at ~90% memory utilization. Scaling to distributed inference requires additional orchestration and may reduce the cost benefit of quantization.
License & commercial use
MIT License. A permissive OSI-approved license that permits use, modification, and distribution for any purpose, including commercial, provided the license text and copyright notice are retained.
MIT is a permissive open-source license; commercial use is explicitly permitted. However, this is a community-contributed quantized derivative. Ensure your organization reviews: (1) the quantization quality/validation for your use case, (2) compliance with the upstream GLM-4 base model (zai-org/GLM-4-32B-0414) license (not provided here), and (3) any data/privacy restrictions in your industry. MIT covers the quantized weights; review base model terms separately.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | Medium |
No security audit or adversarial robustness claims stated. Quantization can alter model behavior in unpredictable ways; test for unintended outputs (e.g., jailbreaks, prompt injection) before production. No mention of data privacy, filtering, or safety mechanisms. Treat as a generic LLM with standard LLM security/misuse risks (hallucination, prompt injection, data leakage if connected to external systems). Calibration data (Pile) is public; no private data exposure apparent.
Alternatives to consider
zai-org/GLM-4-32B-0414 (unquantized)
Full-precision original. Higher accuracy and context quality at the cost of ~128GB VRAM. Better for accuracy-critical workloads; worse for cost-constrained deployments.
Meta Llama 3.1 8B / 70B with GPTQ quantization
Broader community validation, more benchmarks, and larger ecosystem. 8B fits on 16GB GPU; 70B quantized is a middle ground between cost and quality.
Mistral 7B or other smaller MoE models
Smaller footprint, similar or better quality. Trade-off: narrower context window and different tokenizer/API surface.
Ship GLM-4-32B-0414.w4a16-gptq with senior software developers
Clone the model from HuggingFace, benchmark it against your use case on a 32GB GPU, and review the upstream base model license. Start with the vLLM serving example provided, then test accuracy and latency under realistic load.
Talk to DEV.coRelated open-source tools
Surfaced by semantic similarity across the DEV.co open-source index.
Related on DEV.co
Explore the category and the services that help you build with it.
GLM-4-32B-0414.w4a16-gptq FAQ
Can I use this commercially?
What GPU do I need?
How does quantization affect accuracy?
Can I fine-tune this model?
Software development & web development with DEV.co
DEV.co helps companies turn open-source tools like GLM-4-32B-0414.w4a16-gptq into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source llms stack.
Ready to evaluate this quantized LLM?
Clone the model from HuggingFace, benchmark it against your use case on a 32GB GPU, and review the upstream base model license. Start with the vLLM serving example provided, then test accuracy and latency under realistic load.