GLM-4.7 Flash

What GLM-4.7 Flash is for

GLM-4.7 Flash is trained for code. Z.ai released it in 2026-06 with a single 30B size, 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 strong 30B coder that runs on a single 3090/4090, and it is the class of model an editor plugin such as Continue or Tabby points at.

Sizes and memory

The 30B needs about 18 GB of weights at Q4_K_M. That is the whole range; there is no larger or smaller variant in this record. Recommended VRAM adds roughly half again on top of the weights for the KV cache and the runtime.

Size	Weights at Q4_K_M	Recommended VRAM	Smallest card in our guide that holds it
30B	18 GB	27 GB	NVIDIA RTX 3090

Hardware that runs GLM-4.7 Flash

11 of the 19 cards in our hardware guide hold at least one size of GLM-4.7 Flash at Q4_K_M. The cheapest that does is the NVIDIA RTX 3090 at about $700, running the 30B.

The largest size any of them holds is the 30B at 18 GB, on the NVIDIA RTX 3090 (24 GB at 936 GB/s). Bandwidth, not capacity, sets the speed once a size fits, so a card further down the list with faster memory will generate more tokens per second on the same weights than a larger, slower one.

What people use it for

The catalogue lists 4 recorded uses for GLM-4.7 Flash: coding, agentic, chat, tools. It is tagged coding, lightweight, tools.

Where GLM-4.7 Flash sits in the GLM family

Our catalogue holds 5 GLM entries. Ordered by release date, GLM-4.7 Flash (2026-06) is the 4th 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 Flash 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 Flash 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 Flash 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.