Meta AI Q8_0 70B Parameters

VRAM Requirements for
Llama 3.3 70B Q8_0

To run Llama 3.3 70B locally at Q8_0 quantization, you need at minimum 93.2 GB of GPU VRAM.

93.2 GB
Required VRAM
74 GB
File Size
131K tokens
Context Window
70B
Parameters
Estimated VRAM Required
93.2
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 (Q8_0) 74 GB
Context Overhead (131,072 tokens × 70B × 2 ÷ 1M) 18.35 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 93.2 GB

Try a Different Quantization

Use the interactive calculator to compare Llama 3.3 70B across all available formats.

Open Live Calculator →

Llama 3.3 70B — Other Quantizations

Q4_K_M 39 GB file
58.1 GB
Q5_K_M 48 GB file
67.2 GB
Q8_0 Current
93.2 GB
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Frequently Asked Questions

Can I run Llama 3.3 70B Q8_0 on a consumer GPU?
Running Llama 3.3 70B Q8_0 locally requires 93.2 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 Llama 3.3 70B?
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.