Open Buddy FP16 70B Parameters

VRAM Requirements for
openbuddy-nemotron-70b-v23.2-131k-gguf FP16

To run openbuddy-nemotron-70b-v23.2-131k-gguf locally at FP16 quantization, you need at minimum 143.15 GB of GPU VRAM.

143.15 GB
Required VRAM
140 GB
File Size
8K tokens
Context Window
70B
Parameters
Estimated VRAM Required
143.15
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 (FP16) 140 GB
Context Overhead (8,192 tokens × 70B × 2 ÷ 1M) 1.147 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 143.15 GB

Try a Different Quantization

Use the interactive calculator to compare openbuddy-nemotron-70b-v23.2-131k-gguf across all available formats.

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openbuddy-nemotron-70b-v23.2-131k-gguf — Other Quantizations

FP16 Current
143.15 GB
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Frequently Asked Questions

Can I run openbuddy-nemotron-70b-v23.2-131k-gguf FP16 on a consumer GPU?
Running openbuddy-nemotron-70b-v23.2-131k-gguf FP16 locally requires 143.15 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 openbuddy-nemotron-70b-v23.2-131k-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 FP16 quality good enough for production?
FP16/BF16 is the standard precision used for production inference and serves as the quality baseline. All fine-tuned models are typically served at this precision.