Unsloth Q4_K_S 268B Parameters

VRAM Requirements for
GLM-4.6-REAP-268B-A32B-GGUF Q4_K_S

To run GLM-4.6-REAP-268B-A32B-GGUF locally at Q4_K_S quantization, you need at minimum 157.14 GB of GPU VRAM.

157.14 GB
Required VRAM
150.75 GB
File Size
8K tokens
Context Window
268B
Parameters
Estimated VRAM Required
157.14
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_K_S) 150.75 GB
Context Overhead (8,192 tokens × 268B × 2 ÷ 1M) 4.391 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 157.14 GB

Try a Different Quantization

Use the interactive calculator to compare GLM-4.6-REAP-268B-A32B-GGUF across all available formats.

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GLM-4.6-REAP-268B-A32B-GGUF — Other Quantizations

Q2_K 83.75 GB file
90.14 GB
Q2_K_L 83.75 GB file
90.14 GB
Q2_K_XL 83.75 GB file
90.14 GB
Q2_M 83.75 GB file
90.14 GB
Q2_XXS 83.75 GB file
90.14 GB
Q3_K_M 117.25 GB file
123.64 GB
Q3_K_S 117.25 GB file
123.64 GB
Q3_K_XL 117.25 GB file
123.64 GB
Q3_XXS 117.25 GB file
123.64 GB
Q4_K_XL 150.75 GB file
157.14 GB
Q4_K_S Current
157.14 GB
Q4_K_M 150.75 GB file
157.14 GB
Q4_1 150.75 GB file
157.14 GB
Q4_0 150.75 GB file
157.14 GB
Q4_NL 150.75 GB file
157.14 GB
Q4_XS 150.75 GB file
157.14 GB
Q5_K_M 184.25 GB file
190.64 GB
Q5_K_S 184.25 GB file
190.64 GB
Q5_K_XL 184.25 GB file
190.64 GB
Q6_K 217.75 GB file
224.14 GB
Q6_K_XL 217.75 GB file
224.14 GB
Q1_0 268 GB file
274.39 GB
Q1_S 268 GB file
274.39 GB
Q1_M 268 GB file
274.39 GB
Q8_0 284.75 GB file
291.14 GB
Q8_K_XL 284.75 GB file
291.14 GB
BF16 536 GB file
542.39 GB
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Frequently Asked Questions

Can I run GLM-4.6-REAP-268B-A32B-GGUF Q4_K_S on a consumer GPU?
Running GLM-4.6-REAP-268B-A32B-GGUF Q4_K_S locally requires 157.14 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-4.6-REAP-268B-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_K_S 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.