Meta AI Q3_K_M 70B Parameters

VRAM Requirements for
Llama 3.1 70B Q3_K_M

To run Llama 3.1 70B locally at Q3_K_M quantization, you need at minimum 48.4 GB of GPU VRAM.

48.4 GB
Required VRAM
29 GB
File Size
131K tokens
Context Window
70B
Parameters
Estimated VRAM Required
48.4
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 0.4 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 (Q3_K_M) 29 GB
Context Overhead (131,072 tokens × 70B × 2 ÷ 1M) 18.35 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 48.4 GB

Try a Different Quantization

Use the interactive calculator to compare Llama 3.1 70B across all available formats.

Open Live Calculator →

Llama 3.1 70B — Other Quantizations

Q3_K_M Current
48.4 GB
Q4_K_M 39 GB file
58.1 GB
Q5_K_M 48 GB file
67.2 GB
Q8_0 74 GB file
93.2 GB
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Frequently Asked Questions

Can I run Llama 3.1 70B Q3_K_M on a consumer GPU?
Running Llama 3.1 70B Q3_K_M locally requires 48.4 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 3.1 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 Q3_K_M quality good enough for production?
Q3_K_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.