Only with CPU offload

GLM-4.7-Flash 30B-A3B at Q4_K_M needs 18.6 GB but only 10.6 GB is addressable, so about 45% of the layers would stream from system RAM at roughly 60 GB/s. Expect around 16 tokens per second — usable for batch work, painful for chat.

55% on GPU 8K context Q4_K_M · 17.5 GB MIT Released 20 Jan 2026

MIT-licensed 30B-A3B tuned for agentic coding, with a DeepSeek-style latent KV cache. 60–80 tok/s reported on a 4090.

What hardware do I need for GLM-4.7-Flash 30B-A3B? →

The VRAM budget

weights 17.5 GB

Weights 17.5 GB KV cache @ 8K 0.41 GB Runtime overhead 0.6 GB Over budget 8.0 GB past 10.6 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	30.9 GB	31.9 GB	—	~6.4	−0.1% ppl	21.3 GB over
Q6_K	23.8 GB	24.8 GB	—	~9.3	−0.4% ppl	14.2 GB over
Q5_K_M	20.6 GB	21.6 GB	—	~12	−0.8% ppl	11.0 GB over
Q4_K_M	17.5 GB	18.6 GB	—	~16	−1.9% ppl	8.0 GB over
Q3_K_M	14.2 GB	15.2 GB	—	~24	−5.4% ppl	4.6 GB over
Q2_K	12.2 GB	13.2 GB	—	~37	−15% ppl	2.6 GB over

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. This model uses multi-head latent attention, so its cache is a compressed latent rather than full K and V.

How to run it

Ollama llama.cpp LM Studio vLLM

terminal

$ llama-server \
    -hf zai-org/GLM-4.7-Flash:Q4_K_M \
    -c 8192 -ngl 25

The engine underneath most of the others. Every knob is exposed. More on llama.cpp.

01Download is 17.5 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.

03If it falls back to CPU silently, drop the context first, then step down a quantisation.

See all models for this rig Compare with another model