Can I Run Llama 3.1 Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?

Superseded model. Llama 3.1 Family has been superseded by Llama 4. This page is kept for reference; the newer family is a better starting point. View Llama 4 →

Written by Jakub Rusinowski · Last updated July 23, 2024

Yes — comfortably

Yes, comfortably — Llama 3.1 8B Instruct at Q8_0 needs about 10.4 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM), leaving ~5.6 GB spare and running at ~67.6 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~67.6 tok/s

RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models	16 GB
Memory bandwidth	960 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

Llama 3.1 Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM): memory by quantization

Quant	Memory needed	Fits 16 GB?	Max context	Est. speed	Download
F16	17.9 GB	✗ No	—	—	16 GB
Q8_0	10.4 GB	✓ Yes	32K	~67.6 tok/s	8.5 GB
Q6_K	8.4 GB	✓ Yes	64K	~82.6 tok/s	6.6 GB
Q5_K_M	7.5 GB	✓ Yes	64K	~92 tok/s	5.7 GB
Q4_K_M	6.7 GB	✓ Yes	64K	~103.1 tok/s	4.8 GB
Q3_K_M	5.3 GB	✓ Yes	64K	~129.4 tok/s	3.4 GB
Q2_K	4.5 GB	✓ Yes	64K	~150.5 tok/s	2.6 GB

RTX 5080 desktop limitations

16 GB VRAM is the binding constraint, not compute — a slower 24 GB card runs strictly more models.

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
16 GB of VRAM on the NVIDIA GeForce RTX 5080 at 960 GB/s.
32 GB of system RAM available for CPU offload when a model exceeds VRAM.
Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
KV cache is computed from this model's published attention configuration.

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