GLM-4.7

What GLM-4.7 is for

GLM-4.7 is trained for code. Z.ai released it in 2025-12 with no published dense parameter count, 128K of context and a MIT licence. Context length matters more here than for chat, because a coding assistant that cannot see the whole file is guessing. The catalogue records its strength as advanced coding and agentic workflows, and it is the class of model an editor plugin such as Continue or Tabby points at.

Sizes and memory

Our catalogue does not carry a per-size memory breakdown for GLM-4.7, because no dense parameter count has been published for it. What it records instead is a floor of 64 GB and a recommended 96 GB for the default configuration. Until a per-size figure exists, treat those two numbers as the whole guidance rather than reading a size table that would have to be invented.

Hardware that runs GLM-4.7

Without a per-size memory figure there is no honest way to say which card holds GLM-4.7. Its recorded floor of 64 GB rules out every consumer card in our hardware guide, which tops out well below that.

What people use it for

The catalogue lists 3 recorded uses for GLM-4.7: coding, agentic, tool use. It is tagged coding, agentic, MoE.

Where GLM-4.7 sits in the GLM family

Our catalogue holds 5 GLM entries. Ordered by release date, GLM-4.7 (2025-12) is the 1st of them, and GLM-5.2 (2026-06) is newer.

The MIT licence puts no usage ceiling on it, which matters if the thing you are building has users. That is the practical difference between this and a vendor community licence.

Context window: 128K

128K tokens is roughly 96,000 words at the usual ratio, which is a small repository or a whole book. Two cautions come with a window this size: the KV cache at full length can rival the weights for memory, and models rarely use the far end of their advertised context as well as they use the near end. Treat it as headroom, not as a promise.

How to run GLM-4.7 locally

Our catalogue does not record registry tags, so look the current tag up in the Ollama library or on Hugging Face before pasting these. The commands below are the shape of the workflow, not a copy-and-paste recipe.

Option 1: Ollama (simplest)

# find the tag for GLM-4.7 at ollama.com/library
ollama run <tag>

Option 2: Mullama (production)

mullama pull <tag>
mullama run <tag>

Option 3: llama.cpp (CLI)

# download a GGUF from Hugging Face, searching for "GLM-4.7 GGUF"
./llama-cli -m model.Q4_K_M.gguf -p "Hello, AI!"

Option 4: Python with Mullama or llama-cpp-python

from mullama import Model, Context
model = Model.load("model.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256))

Sources

Model record from src/data/models.json (2026-06-29); card capacities from src/data/gpus.json (2026-06-29). Upstream: Curated from ollama.com/library + community benchmarks + paperswithcode + huggingface.