David A U FP4 120B Parameters

VRAM Requirements for
Openai_gpt-oss-120b-NEO-Imatrix-GGUF FP4

To run Openai_gpt-oss-120b-NEO-Imatrix-GGUF locally at FP4 quantization, you need at minimum 123.97 GB of GPU VRAM.

123.97 GB
Required VRAM
120 GB
File Size
8K tokens
Context Window
120B
Parameters
Estimated VRAM Required
123.97
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 (FP4) 120 GB
Context Overhead (8,192 tokens × 120B × 2 ÷ 1M) 1.966 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 123.97 GB

Try a Different Quantization

Use the interactive calculator to compare Openai_gpt-oss-120b-NEO-Imatrix-GGUF across all available formats.

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Openai_gpt-oss-120b-NEO-Imatrix-GGUF — Other Quantizations

Q4_NL 67.5 GB file
71.47 GB
FP4 Current
123.97 GB
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Frequently Asked Questions

Can I run Openai_gpt-oss-120b-NEO-Imatrix-GGUF FP4 on a consumer GPU?
Running Openai_gpt-oss-120b-NEO-Imatrix-GGUF FP4 locally requires 123.97 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 Openai_gpt-oss-120b-NEO-Imatrix-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.