Bartowski Q8_0 82B Parameters

VRAM Requirements for
cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF Q8_0

To run cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF locally at Q8_0 quantization, you need at minimum 90.47 GB of GPU VRAM.

90.47 GB
Required VRAM
87.13 GB
File Size
8K tokens
Context Window
82B
Parameters
Estimated VRAM Required
90.47
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 (Q8_0) 87.13 GB
Context Overhead (8,192 tokens × 82B × 2 ÷ 1M) 1.343 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 90.47 GB

Try a Different Quantization

Use the interactive calculator to compare cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF across all available formats.

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cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF — Other Quantizations

Q2_K 25.63 GB file
28.97 GB
Q2_K_L 25.63 GB file
28.97 GB
Q2_M 25.63 GB file
28.97 GB
Q2_S 25.63 GB file
28.97 GB
Q2_XS 25.63 GB file
28.97 GB
Q2_XXS 25.63 GB file
28.97 GB
Q3_XS 35.88 GB file
39.22 GB
Q3_XXS 35.88 GB file
39.22 GB
Q3_M 35.88 GB file
39.22 GB
Q3_K_XL 35.88 GB file
39.22 GB
Q3_K_S 35.88 GB file
39.22 GB
Q3_K_M 35.88 GB file
39.22 GB
Q3_K_L 35.88 GB file
39.22 GB
Q4_0 46.13 GB file
49.47 GB
Q4_1 46.13 GB file
49.47 GB
Q4_K_L 46.13 GB file
49.47 GB
Q4_K_M 46.13 GB file
49.47 GB
Q4_K_S 46.13 GB file
49.47 GB
Q4_NL 46.13 GB file
49.47 GB
Q4_XS 46.13 GB file
49.47 GB
Q5_K_M 56.38 GB file
59.72 GB
Q5_K_S 56.38 GB file
59.72 GB
Q6_K 66.63 GB file
69.97 GB
Q1_M 82 GB file
85.34 GB
Q8_0 Current
90.47 GB
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Frequently Asked Questions

Can I run cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF Q8_0 on a consumer GPU?
Running cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF Q8_0 locally requires 90.47 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-4.5-Air-REAP-82B-A12B-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 Q8_0 quality good enough for production?
Q8_0 produces near-lossless quality compared to FP16. It's widely used in production deployments where quality is critical and you can afford the extra VRAM.