How much VRAM does GLM-4.7-Flash need?

About 19.2 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 20.8 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will GLM-4.7-Flash run on a 24GB GPU?

Yes — at Q4_K_M and 4K context it needs about 19.2 GB, which leaves 4.8 GB spare on a RTX 3090, or on any of the other 24 GB cards here (4 in all). That is the smallest size in this index that clears it comfortably; 26 of 59 do.

Which quantization of GLM-4.7-Flash should I use?

No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 2 levels in the index are Q4_K_M, Q8_0. Note this is a mixture-of-experts model — all parameters must be resident even though only a fraction are active per token, so the memory cost follows the total, not the active count.

GLM-4.7-Flash

30B-A3B

Zhipu GLM-4.7

Zhipu's lightweight member of GLM-4.7: 30B total, about 3B active per token, MIT licence. Its attention is MLA, which caches one compressed 576-value vector per token per layer instead of full keys and values — so at 32K context the cache is under 2 GB where a conventional 47-layer model would need several times that. llama.cpp runs it as a DeepSeek-2-style model. Sizes below use the calculator's generic Q4_K_M/Q8_0 rates: no GGUF file size could be checked, and quality loss per level has not been published.

⬇ 1.8M HF downloads♥ 1856 likeszai-org/GLM-4.7-Flash· stats from 9/30/2026

Consumer GPUPro GPUMac / Apple Silicon

131K

Max Context

Quant Variants

GGUF Q8_0

Best Quality

—

Accuracy Retained

Calculate VRAM Hugging Face Compare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	4.85	18.2 GB	—	—	Estimated	Calc HF
GGUF	Q8_0	8.5	31.8 GB	—	—	Estimated	Calc HF

Similar models

Compare with Seed-OSS 36B

36B

Seed-OSS 36B Instruct

ByteDance Seed

Consumer GPUPro GPU

17.4 GBmin VRAM98.7%accuracy

Dense 36B with a native 512K context. Q4 weights are ~22GB, which lands on a 24GB card or a 2×16GB split — but the context is the real cost: 512K of KV cache is roughly 128GB on its own, so budget context first and weights second.

27B

Qwen3.8 27B

Alibaba Qwen3.8

Consumer GPUPro GPU

15.3 GBmin VRAM—accuracy

Hybrid attention: only 16 of its 64 layers keep a KV cache, which is why a 27B model holds a 262K context in about 16GB of cache rather than 64GB. Sizing it as a conventional stack overstates the cache fourfold — the estimates here count the 16 and match published measurements at 8K, 32K and 262K. Quality loss per level has not been published.

32B

Qwen2.5 32B Instruct

Alibaba Qwen2.5

Consumer GPUPro GPU

16.4 GBmin VRAM97.3%accuracy

Near-GPT-4 reasoning on a 24GB VRAM card (Q4_K_S). Groundbreaking value.

32B

DeepSeek-R1-Distill-Qwen-32B

DeepSeek

Consumer GPUPro GPU

16.8 GBmin VRAM97.4%accuracy

R1 distilled to 32B. Near-frontier reasoning on a single 24GB card (Q3/Q4).

Running GLM-4.7-Flash locally

At Q4_K_M and 4K of context, GLM-4.7-Flash needs about 19.2 GB — 17.2 GB of weights, 0.21 GB of KV cache and a 1.7 GB activation buffer. The smallest cards in this index that clear that comfortably are 24 GB ones — 4 of them, from the RTX 3090 up — and 26 of the 59 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 1.44 GB, taking the total to 20.8 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 26 GB.

Which build to download

This index only tracks GLM-4.7-Flash in GGUF, across 2 levels (Q4_K_M, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 and CUDA only.

Common questions

How much VRAM does GLM-4.7-Flash need?: About 19.2 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 20.8 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will GLM-4.7-Flash run on a 24GB GPU?: Yes — at Q4_K_M and 4K context it needs about 19.2 GB, which leaves 4.8 GB spare on a RTX 3090, or on any of the other 24 GB cards here (4 in all). That is the smallest size in this index that clears it comfortably; 26 of 59 do.
Which quantization of GLM-4.7-Flash should I use?: No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 2 levels in the index are Q4_K_M, Q8_0. Note this is a mixture-of-experts model — all parameters must be resident even though only a fraction are active per token, so the memory cost follows the total, not the active count.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Runs comfortably on: RTX 309024GB · +4.8 Radeon RX 7900 XTX24GB · +4.8 RTX 409024GB · +4.8 Tesla P40 24G24GB · +4.8 Mac M5 32G32GB · +4.8
Tight but possible: Radeon RX 7900 XT20GB
Just misses: Mac M4 Pro 24G+1.2GB over RTX 4060 Ti 16G+3.2GB over
Ships in: GGUF
Try it yourself: Size it in the calculator Generate the run command

How to actually run this

Deployment guides for this model and this class of hardware.

This page's figures change when the model or the runtime does.Last updated 2026-09-30 RSS → /feed.xml