Unsloth Q2_M 218B Parameters

VRAM Requirements for
GLM-4.7-REAP-218B-A32B-GGUF Q2_M

To run GLM-4.7-REAP-218B-A32B-GGUF locally at Q2_M quantization, you need at minimum 73.7 GB of GPU VRAM.

73.7 GB
Required VRAM
68.13 GB
File Size
8K tokens
Context Window
218B
Parameters
Estimated VRAM Required
73.7
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 25.7 GB short
Budget $3,200 – $4,000

2× RTX 4090 (48GB) + aggressive quant

48 GB

Use a lower quantization to fit. Viable for testing at this scale.

Balanced $15,000 – $22,000

NVIDIA A100 80GB PCIe

80 GB HBM2e

Single-card 80GB. Industry-standard for large model inference.

Ultimate $25,000 – $40,000

NVIDIA H100 80GB SXM5

80 GB HBM3

State-of-the-art inference. 3× the bandwidth of A100.

📊 VRAM Calculation Breakdown

Model File Size (Q2_M) 68.13 GB
Context Overhead (8,192 tokens × 218B × 2 ÷ 1M) 3.572 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 73.7 GB

Try a Different Quantization

Use the interactive calculator to compare GLM-4.7-REAP-218B-A32B-GGUF across all available formats.

Open Live Calculator →

GLM-4.7-REAP-218B-A32B-GGUF — Other Quantizations

Q2_K 68.13 GB file
73.7 GB
Q2_K_L 68.13 GB file
73.7 GB
Q2_K_XL 68.13 GB file
73.7 GB
Q2_M Current
73.7 GB
Q2_XXS 68.13 GB file
73.7 GB
Q3_K_M 95.38 GB file
100.95 GB
Q3_K_S 95.38 GB file
100.95 GB
Q3_K_XL 95.38 GB file
100.95 GB
Q3_XXS 95.38 GB file
100.95 GB
Q4_K_XL 122.63 GB file
128.2 GB
Q4_K_S 122.63 GB file
128.2 GB
Q4_K_M 122.63 GB file
128.2 GB
Q4_1 122.63 GB file
128.2 GB
Q4_0 122.63 GB file
128.2 GB
Q4_NL 122.63 GB file
128.2 GB
Q4_XS 122.63 GB file
128.2 GB
Q5_K_M 149.88 GB file
155.45 GB
Q5_K_S 149.88 GB file
155.45 GB
Q5_K_XL 149.88 GB file
155.45 GB
Q6_K 177.13 GB file
182.7 GB
Q6_K_XL 177.13 GB file
182.7 GB
Q1_0 218 GB file
223.57 GB
Q1_S 218 GB file
223.57 GB
Q1_M 218 GB file
223.57 GB
Q8_0 231.63 GB file
237.2 GB
Q8_K_XL 231.63 GB file
237.2 GB
BF16 436 GB file
441.57 GB
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Frequently Asked Questions

Can I run GLM-4.7-REAP-218B-A32B-GGUF Q2_M on a consumer GPU?
Running GLM-4.7-REAP-218B-A32B-GGUF Q2_M locally requires 73.7 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run GLM-4.7-REAP-218B-A32B-GGUF?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is Q2_M quality good enough for production?
Q2_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.