Implement · GLM-4-9B-Chat
Can you run it?
Ownership levelLimitednone·limited·partial·substantial·fullAnalytical input D ยท 52.8/100
This page is a projection of the one entry record, the Reliability and Data control factors that Implement covers. The full verdict is set by all four factors together, floor-weighted so the weakest caps the whole.
Which domain expands which factor
- AssessUse & modify + Transparency
- ImplementData control + Reliability
- UseReliability
- SupportTransparency
Install & run
Download the safetensors from the verified THUDM organisation on Hugging Face and serve with
transformers, vLLM, llama.cpp, or Ollama; it runs on a single consumer GPU. Complete the
glm-4 commercial registration first if your use is commercial. Pin the exact revision and
verify checksums.
Hardware & VRAM requirements
A 9B dense model: a single consumer GPU, or CPU when quantized. The GLM-4-9B-Chat-1M variant
advertises a 1M-token context, which is memory-intensive in practice; the standard Chat is
128K.
Serving stacks
vLLM, transformers, llama.cpp, and Ollama - broad support for a small 2024
model, though the tooling is older than the current GLM line.
Safe-deployment controls & Deployment Ceiling
Deployment Ceiling: T2 (conditional). First, complete the commercial registration and
honour the glm-4 naming and field-of-use terms - these are binding licence conditions - and
account for the revocable grant in your risk assessment. Then: supply your own input/output
guardrails and a guard/classifier model (China-aligned alignment, no first-party guard), and
add prompt-injection defences for agentic use.
Available quantizations
Widely quantized (GGUF and lower-bit) as a small model. Redistribution of these derivatives
must carry the glm-4 licence and the name rules - verify provenance per mirror.
Fine-tuning & adaptation
Fine-tuning is permitted, but derivatives inherit the revocable, registration-gated licence
and the mandatory "glm-4" name prefix. The training data and code are closed, so there is no
from-scratch reproduction.
API / OpenAI-compatible integration
Serve behind vLLM's OpenAI-compatible endpoint. The 2024-era chat template and tool-call
conventions are older than the current GLM line - confirm behaviour per serving stack.
How this scores
The ownership factors this domain covers, drawn from the one entry record.
3
ReliabilityIs it reliable and good enough for the job?
ModeratePerformance 2 (dated, superseded), operational 4, safety 3: runnable and broadly supported, but the below-average current capability holds reliability to moderate.
How this scores (AOI sub-dimensions)
Operational4/5how practical it is to run, serve and maintain in productionA small 9B dense model with broad serving (vLLM, transformers, llama.cpp, Ollama) and extensive community quants, runnable on a single GPU.
Safety3/5whether misuse risks are evaluated and guardrails are providedA safety-tuned chat model with documented behaviour, meeting the score-3 anchor, but with China-aligned alignment, no first-party guard model, and older/lighter tuning than current frontier practice - deployers must add their own guardrails.
4
Doesn't extract your dataDoes running it keep your knowledge and data yours?
ModerateSelf-hosting keeps your processing local, but the licence grant is revocable and commercial use hinges on a registration the publisher controls - so your control over continued use is contingent, not assured. Moderate, not strong.
How this scores
Not a scored AOI dimension. For a self-hosted model, data-control is a structural property of running the weights yourself, strong by default unless the model phones home or the licence claws back rights. For a hosted API this factor is the retention + train-on-inputs + residency read, scored from the binding terms.
What this means for adoptionYou have only limited ownership of the original GLM-4-9B-Chat: the custom glm-4 licence is revocable, gates commercial use behind registration, restricts fields of use, and forces a 'glm-4' name prefix and 'Built with glm-4' attribution on every derivative - so use-and-modify is weak and your continued-use control is contingent. Combined with dated capability, ownership lands at limited, the low end of the GLM family and well below the MIT GLM line (the `glm` entry, substantial). Use this checkpoint only if you specifically need it: complete the commercial registration, honour the naming and field-of-use terms, and account for the revocable grant - otherwise adopt the MIT GLM models instead.
Sources
The same evidence records as the entry sheet. Read means the text was verified; unverified means it is known to exist but not yet read.
Licenceread2026-08-03
THUDM/glm-4-9b LICENSE, read: [Sec 2] "a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty-free copyright license"; free for academic research; "Commercial users must complete registration ...
Model cardread2026-08-03
GLM-4-9B-Chat model card on the verified THUDM HF org: a 9B dense chat model with a 128K context (and a 1M-context GLM-4-9B-Chat-1M variant), safetensors; broad serving (vLLM, transformers, llama.cpp, Ollama) and extensive community quants; a mid-2024 release.
Third-party analysisunverified2026-08-03
GLM-4-9B-Chat was a capable small model at its mid-2024 release but is dated by 2026 and superseded within its own family by GLM-4-9B-0414 and the 4.5/4.6 line.
Third-party analysisunverified2026-08-03
GLM chat models carry China-aligned alignment on politically sensitive topics and ship no companion guard model; the original 9B's safety tuning is older/lighter than current practice.
Third-party analysisunverified2026-08-03
The glm-4 licence is revocable and gates commercial use behind registration with field-of-use and naming restrictions (non-FOSS), so no open-source exemption applies; no EU copyright policy or training-content summary is published, and the licence is governed by PRC law.