Bartowski Q4_0 218B Parameters

VRAM Requirements for
cerebras_GLM-4.7-REAP-218B-A32B-GGUF Q4_0

To run cerebras_GLM-4.7-REAP-218B-A32B-GGUF locally at Q4_0 quantization, you need at minimum 128.2 GB of GPU VRAM.

128.2 GB
Required VRAM
122.63 GB
File Size
8K tokens
Context Window
218B
Parameters
Estimated VRAM Required
128.2
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

Ultimate $3,000,000+

NVIDIA GB200 NVL72

1.4 TB HBM3e

Next-gen Grace Blackwell Superchip. Built for frontier models.

📊 VRAM Calculation Breakdown

Model File Size (Q4_0) 122.63 GB
Context Overhead (8,192 tokens × 218B × 2 ÷ 1M) 3.572 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 128.2 GB

Try a Different Quantization

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

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cerebras_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_M 68.13 GB file
73.7 GB
Q2_S 68.13 GB file
73.7 GB
Q2_XS 68.13 GB file
73.7 GB
Q2_XXS 68.13 GB file
73.7 GB
Q3_K_L 95.38 GB file
100.95 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_M 95.38 GB file
100.95 GB
Q3_XS 95.38 GB file
100.95 GB
Q3_XXS 95.38 GB file
100.95 GB
Q4_K_M 122.63 GB file
128.2 GB
Q4_XS 122.63 GB file
128.2 GB
Q4_K_S 122.63 GB file
128.2 GB
Q4_NL 122.63 GB file
128.2 GB
Q4_1 122.63 GB file
128.2 GB
Q4_0 Current
128.2 GB
Q5_K_M 149.88 GB file
155.45 GB
Q5_K_S 149.88 GB file
155.45 GB
Q6_K 177.13 GB file
182.7 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
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Frequently Asked Questions

Can I run cerebras_GLM-4.7-REAP-218B-A32B-GGUF Q4_0 on a consumer GPU?
Running cerebras_GLM-4.7-REAP-218B-A32B-GGUF Q4_0 locally requires 128.2 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 cerebras_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 Q4_0 quality good enough for production?
Q4_K_M is an excellent balance of quality and performance. Perplexity tests show minimal degradation (< 2%) vs FP16 for most models. Suitable for most production applications.