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Can I run Llama 3.1 70B (Q4_K_M (GGUF 4-bit)) on NVIDIA A100 80GB?

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Perfect
Yes, you can run this model!
GPU VRAM
80.0GB
Required
35.0GB
Headroom
+45.0GB

VRAM Usage

0GB 44% used 80.0GB

Performance Estimate

Tokens/sec ~54.0
Batch size 3
Context 128000K

info Technical Analysis

NVIDIA A100 80GB provides excellent compatibility with Llama 3.1 70B (70.00B). With 80.0GB of VRAM and only 35.0GB required, you have 45.0GB of headroom for comfortable inference. This allows for extended context lengths, batch processing, and smooth operation.

lightbulb Recommendation

You can run Llama 3.1 70B (70.00B) on NVIDIA A100 80GB without any compromises. Consider using full context length and larger batch sizes for optimal throughput.

tune Recommended Settings

Batch_Size
3
Context_Length
128000
Inference_Framework
llama.cpp or vLLM

help Frequently Asked Questions

Can I run Llama 3.1 70B (70.00B) on NVIDIA A100 80GB? expand_more
NVIDIA A100 80GB has 80.0GB VRAM, which provides 45.0GB of headroom beyond the 35.0GB required by Llama 3.1 70B (70.00B). This is plenty of room for comfortable inference with room for KV cache, batching, and extended context lengths.
How much VRAM does Llama 3.1 70B (70.00B) need? expand_more
Llama 3.1 70B (70.00B) requires approximately 35.0GB of VRAM.
What performance can I expect? expand_more
Estimated 54 tokens per second.

GPU

memory
NVIDIA A100 80GB
80.0GB VRAM

AI Model

smart_toy
Llama 3.1 70B (70.00B)
70.00B params

Alternative Quantizations

q3_k_m Perfect INT8 (8-bit Integer) Perfect

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