Andyjack FP4 252B Parameters

VRAM Requirements for
GLM-4.6-REAP-252B-A32B-MXFP4_MOE_GGUF FP4

To run GLM-4.6-REAP-252B-A32B-MXFP4_MOE_GGUF locally at FP4 quantization, you need at minimum 258.13 GB of GPU VRAM.

258.13 GB
Required VRAM
252 GB
File Size
8K tokens
Context Window
252B
Parameters
Estimated VRAM Required
258.13
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 98.1 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 98.1 GB short
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 (FP4) 252 GB
Context Overhead (8,192 tokens × 252B × 2 ÷ 1M) 4.129 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 258.13 GB

Try a Different Quantization

Use the interactive calculator to compare GLM-4.6-REAP-252B-A32B-MXFP4_MOE_GGUF across all available formats.

Open Live Calculator →

GLM-4.6-REAP-252B-A32B-MXFP4_MOE_GGUF — Other Quantizations

FP4 Current
258.13 GB
Advertisement Zone

Frequently Asked Questions

Can I run GLM-4.6-REAP-252B-A32B-MXFP4_MOE_GGUF FP4 on a consumer GPU?
Running GLM-4.6-REAP-252B-A32B-MXFP4_MOE_GGUF FP4 locally requires 258.13 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-252B-A32B-MXFP4_MOE_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 FP4 quality good enough for production?
FP4 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.