Verdugie Q8_0 72B Parameters

VRAM Requirements for
Opus-Candid-70B-V1 Q8_0

To run Opus-Candid-70B-V1 locally at Q8_0 quantization, you need at minimum 79.68 GB of GPU VRAM.

79.68 GB
Required VRAM
76.5 GB
File Size
8K tokens
Context Window
72B
Parameters
Estimated VRAM Required
79.68
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 31.7 GB short
Budget $3,200 – $4,000

2× RTX 4090 (48GB) + aggressive quant

48 GB

Use a lower quantization to fit. Viable for testing at this scale.

Balanced $15,000 – $22,000

NVIDIA A100 80GB PCIe

80 GB HBM2e

Single-card 80GB. Industry-standard for large model inference.

Ultimate $25,000 – $40,000

NVIDIA H100 80GB SXM5

80 GB HBM3

State-of-the-art inference. 3× the bandwidth of A100.

📊 VRAM Calculation Breakdown

Model File Size (Q8_0) 76.5 GB
Context Overhead (8,192 tokens × 72B × 2 ÷ 1M) 1.18 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 79.68 GB

Try a Different Quantization

Use the interactive calculator to compare Opus-Candid-70B-V1 across all available formats.

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Opus-Candid-70B-V1 — Other Quantizations

Q4_K_M 40.5 GB file
43.68 GB
Q8_0 Current
79.68 GB
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Frequently Asked Questions

Can I run Opus-Candid-70B-V1 Q8_0 on a consumer GPU?
Running Opus-Candid-70B-V1 Q8_0 locally requires 79.68 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 Opus-Candid-70B-V1?
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.