Can RTX 5080 Run LLaMA 3 70B?

Can RTX 5080 Run LLaMA 3 70B?

No, the RTX 5080 cannot run LLaMA 3 70B in any practical configuration. The RTX 5080 has 16 GB of GDDR7 VRAM, while LLaMA 3 70B requires a minimum of 38 GB even at 4-bit quantization. The model simply does not fit. However, the 5080 is excellent for LLaMA 3 8B at full FP16 precision on a dedicated GPU server.

The RTX 5080 brings Blackwell architecture improvements including faster memory bandwidth (~960 GB/s with GDDR7) and better FP8 support compared to Ada Lovelace. These improvements make it a strong card for models that fit within 16 GB, but 70B is beyond its reach without multi-GPU setups.

VRAM Analysis: 16 GB vs 70B Parameters

Even at 2-bit quantization (where quality degrades heavily), 70B still needs about 20 GB. The RTX 5080’s 16 GB is insufficient by a wide margin. See our LLaMA 3 VRAM requirements page for the full analysis.

What LLaMA 3 Models Fit on RTX 5080?

The 16 GB of GDDR7 puts the RTX 5080 in an excellent position for 8B class models:

• LLaMA 3 8B FP16: Fits with short context (2048-3072 tokens). Best quality.
• LLaMA 3 8B INT8: Fits comfortably with 8K context. Minimal quality loss.
• LLaMA 3 8B 4-bit: Fits with very long context (16K+). Ideal for document processing.
• LLaMA 3 70B: Does not fit at any quantization level.
• LLaMA 3 405B: Does not fit at any quantization level.

The 5080 is also excellent for running other 7B-14B models. Check our pages on Mistral 7B compatibility and Mistral VRAM requirements for alternatives in this size range.

Performance Benchmarks

The RTX 5080’s Blackwell architecture delivers strong performance within its VRAM tier:

The Blackwell architecture’s improved FP8 tensor cores make FP8 inference particularly efficient, offering near-FP16 quality at INT8-like speeds. Compare these numbers on our tokens per second benchmark page.

Quantization Options for 16 GB

With 16 GB, you have flexible quantization options for the 8B model:

FP8 is the standout option on the RTX 5080 thanks to native hardware support. It nearly matches FP16 quality while using roughly half the VRAM. Read more about quantization in our quantization format comparison.

Setup Commands

Ollama

# LLaMA 3 8B with auto quantization
curl -fsSL https://ollama.com/install.sh | sh
ollama run llama3:8b

vLLM with FP8 (Optimal for 5080)

# Serve with FP8 quantization for Blackwell
pip install vllm
vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
  --quantization fp8 --max-model-len 8192 \
  --gpu-memory-utilization 0.90

For deployment guides, see our Ollama hosting and vLLM hosting pages. The self-host LLM guide walks through the full setup process.

GPU Alternatives for 70B Models

If you need to run LLaMA 3 70B, here are the realistic options:

See our RTX 3090 LLaMA 3 70B analysis and RTX 5090 70B FP16 analysis for detailed breakdowns. For cost comparisons, use our cost per million tokens calculator.