Dev Quasar 7 Q8_0 670B Parameters

VRAM Requirements for
oumi-ai.distill-r1-670b-math-GGUF Q8_0

To run oumi-ai.distill-r1-670b-math-GGUF locally at Q8_0 quantization, you need at minimum 724.86 GB of GPU VRAM.

724.86 GB
Required VRAM
711.88 GB
File Size
8K tokens
Context Window
670B
Parameters
Estimated VRAM Required
724.86
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 564.9 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 564.9 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 (Q8_0) 711.88 GB
Context Overhead (8,192 tokens × 670B × 2 ÷ 1M) 10.977 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 724.86 GB

Try a Different Quantization

Use the interactive calculator to compare oumi-ai.distill-r1-670b-math-GGUF across all available formats.

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oumi-ai.distill-r1-670b-math-GGUF — Other Quantizations

Q2_K 209.38 GB file
222.36 GB
Q3_K_M 293.13 GB file
306.11 GB
Q4_K_M 376.88 GB file
389.86 GB
Q5_K_M 460.63 GB file
473.61 GB
Q6_K 544.38 GB file
557.36 GB
Q8_0 Current
724.86 GB
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Frequently Asked Questions

Can I run oumi-ai.distill-r1-670b-math-GGUF Q8_0 on a consumer GPU?
Running oumi-ai.distill-r1-670b-math-GGUF Q8_0 locally requires 724.86 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 oumi-ai.distill-r1-670b-math-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 Q8_0 quality good enough for production?
Q8_0 produces near-lossless quality compared to FP16. It's widely used in production deployments where quality is critical and you can afford the extra VRAM.