Bartowski Q6_K_L 32B Parameters

VRAM Requirements for
arcee-ai_GLM-4-32B-Base-32K-GGUF Q6_K_L

To run arcee-ai_GLM-4-32B-Base-32K-GGUF locally at Q6_K_L quantization, you need at minimum 28.52 GB of GPU VRAM.

28.52 GB
Required VRAM
26 GB
File Size
8K tokens
Context Window
32B
Parameters
Estimated VRAM Required
28.52
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (Q6_K_L) 26 GB
Context Overhead (8,192 tokens × 32B × 2 ÷ 1M) 0.524 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 28.52 GB

Try a Different Quantization

Use the interactive calculator to compare arcee-ai_GLM-4-32B-Base-32K-GGUF across all available formats.

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arcee-ai_GLM-4-32B-Base-32K-GGUF — Other Quantizations

Q2_K 10 GB file
12.52 GB
Q2_K_L 10 GB file
12.52 GB
Q2_M 10 GB file
12.52 GB
Q2_S 10 GB file
12.52 GB
Q2_XS 10 GB file
12.52 GB
Q2_XXS 10 GB file
12.52 GB
Q3_XXS 14 GB file
16.52 GB
Q3_XS 14 GB file
16.52 GB
Q3_M 14 GB file
16.52 GB
Q3_K_XL 14 GB file
16.52 GB
Q3_K_M 14 GB file
16.52 GB
Q3_K_L 14 GB file
16.52 GB
Q3_K_S 14 GB file
16.52 GB
Q4_K_S 18 GB file
20.52 GB
Q4_XS 18 GB file
20.52 GB
Q4_NL 18 GB file
20.52 GB
Q4_K_M 18 GB file
20.52 GB
Q4_K_L 18 GB file
20.52 GB
Q4_1 18 GB file
20.52 GB
Q4_0 18 GB file
20.52 GB
Q5_K_L 22 GB file
24.52 GB
Q5_K_M 22 GB file
24.52 GB
Q5_K_S 22 GB file
24.52 GB
Q6_K 26 GB file
28.52 GB
Q6_K_L Current
28.52 GB
Q8_0 34 GB file
36.52 GB
BF16 64 GB file
66.52 GB
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Frequently Asked Questions

Can I run arcee-ai_GLM-4-32B-Base-32K-GGUF Q6_K_L on a consumer GPU?
Running arcee-ai_GLM-4-32B-Base-32K-GGUF Q6_K_L locally requires 28.52 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 arcee-ai_GLM-4-32B-Base-32K-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 Q6_K_L quality good enough for production?
Q6_K_L is suitable for specialized use cases. Check community benchmarks for specific quality metrics.