Llama-3.1 70B VRAM Requirements
Developed by Meta
Find out exactly how much VRAM you need to run Llama-3.1 70B locally. Calculate the memory footprint of different GGUF quantization variants (like Q4_K_M or Q8_0), estimate your context length KV cache VRAM footprint, and determine if your hardware supports a full GPU VRAM offload or if you will need to rely on slow partial CPU offloading to avoid a CUDA Out of Memory (OOM) error.
Hardware Configuration
Adjust settings to check compatibility with your system in real time.
Available: 14.50 GB
Available: 29.00 GB
This configuration exceeds your system's usable memory capacity. Attempting to run it will cause crashes or freeze your machine.
Requires 48.60 GB total memory (weights: 42.50 GB, context overhead: 2.50 GB, activation overhead: 3.60 GB).
Llama-3.1 70B Quantization Formats & VRAM Compatibility
Select a format to set it as active and calculate your system fit dynamically.
| Quant | Weights | KV Cache | Overhead | Total VRAM | Status | Links |
|---|---|---|---|---|---|---|
| Base (Unquantized) | 131.42 GB | 2.50 GB | 10.71 GB | 144.63 GB | Too Large | HF weights |
| Q4_K_M | 42.50 GB | 2.50 GB | 3.60 GB | 48.60 GB | Too Large | GGUF |
| Q8_0 | 75.00 GB | 2.50 GB | 6.20 GB | 83.70 GB | Too Large | GGUF |
Llama-3.1 70B KV Cache Memory Breakdown
How to Setup and Run Llama-3.1 70B Locally
Method A: Ollama (Recommended)
Ollama is the easiest way to run models in the background. First, download it from ollama.com, then execute this terminal command:
ollama run <model-name>Method B: LM Studio (GUI)
If you prefer a full graphical interface with chat UI and local server hosting:
- Download and install LM Studio.
- Search for Llama-3.1 70B in the home page search tab.
- Select a quantization level (like Q4_K_M) that fits your VRAM, click download, and load it to chat.
Model Specs
- DeveloperMeta
- Parameter Count70B
- Base File Size131.4 GB
- AvailabilityLocal-Only
- Input ModalitiesText
Standard Benchmark Scores
Commercial API Pricing
This model is self-hosted only or official API pricing is not available.
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