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NVIDIA GeForce RTX 5080

NVIDIA · 16GB GDDR7 · Can run 49 models

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Manufacturer NVIDIA
VRAM 16 GB
Memory Type GDDR7
Architecture Blackwell
CUDA Cores 10,752
Tensor Cores 336
Bandwidth 960 GB/s
TDP 360W
MSRP $999
Released Jan 30, 2025

AI Notes

The RTX 5080 offers a strong balance of performance and VRAM for local AI. With 16GB of GDDR7, it comfortably runs 13B-parameter models and can handle some 30B models with aggressive quantization. Blackwell architecture tensor cores provide excellent inference speed for its price tier.

Compatible Models

Model Parameters Best Quant VRAM Used Fit Est. Speed
Qwen 3 0.6B 600M Q4_K_M 2.5 GB Runs ~384 tok/s
Gemma 3 1B 1B Q8_0 2 GB Runs ~480 tok/s
Llama 3.2 1B 1B Q8_0 3 GB Runs ~320 tok/s
DeepSeek R1 1.5B 1.5B Q8_0 3 GB Runs ~320 tok/s
Gemma 2 2B 2B Q8_0 4 GB Runs ~240 tok/s
Gemma 3n E2B 2B Q4_K_M 3.3 GB Runs ~291 tok/s
Llama 3.2 3B 3B Q8_0 5 GB Runs ~192 tok/s
Phi-3 Mini 3.8B 3.8B Q8_0 5.8 GB Runs ~166 tok/s
Phi-4 Mini 3.8B 3.8B Q4_K_M 4.5 GB Runs ~213 tok/s
Gemma 3 4B 4B Q4_K_M 5 GB Runs ~192 tok/s
Gemma 3n E4B 4B Q4_K_M 4.5 GB Runs ~213 tok/s
Qwen 3 4B 4B Q4_K_M 4.5 GB Runs ~213 tok/s
DeepSeek R1 7B 7B Q8_0 9 GB Runs ~107 tok/s
Falcon 3 7B 7B Q4_K_M 6.8 GB Runs ~141 tok/s
Mistral 7B 7B Q8_0 9 GB Runs ~107 tok/s
Qwen 2.5 7B 7B Q8_0 9 GB Runs ~107 tok/s
Qwen 2.5 Coder 7B 7B Q8_0 9 GB Runs ~107 tok/s
Qwen 2.5 VL 7B 7B Q4_K_M 7 GB Runs ~137 tok/s
Cogito 8B 8B Q4_K_M 7.5 GB Runs ~128 tok/s
DeepSeek R1 8B 8B Q4_K_M 7.5 GB Runs ~128 tok/s
Llama 3.1 8B 8B Q8_0 10 GB Runs ~96 tok/s
Nemotron 3 Nano 8B 8B Q4_K_M 7.5 GB Runs ~128 tok/s
Qwen 3 8B 8B Q4_K_M 7.5 GB Runs ~128 tok/s
Gemma 2 9B 9B Q8_0 11 GB Runs ~87 tok/s
Falcon 3 10B 10B Q4_K_M 8.5 GB Runs ~113 tok/s
Llama 3.2 Vision 11B 11B Q4_K_M 8.5 GB Runs ~113 tok/s
Gemma 3 12B 12B Q4_K_M 10.5 GB Runs ~91 tok/s
Mistral Nemo 12B 12B Q4_K_M 9.5 GB Runs ~101 tok/s
DeepSeek R1 14B 14B Q4_K_M 9.9 GB Runs ~97 tok/s
Phi-4 14B 14B Q4_K_M 9.9 GB Runs ~97 tok/s
Phi-4 Reasoning 14B 14B Q4_K_M 11 GB Runs ~87 tok/s
Qwen 2.5 14B 14B Q4_K_M 9.9 GB Runs ~97 tok/s
Qwen 2.5 Coder 14B 14B Q4_K_M 12 GB Runs ~80 tok/s
Qwen 3 14B 14B Q4_K_M 12 GB Runs ~80 tok/s
Codestral 22B 22B Q4_K_M 14.7 GB Runs (tight) ~65 tok/s
StarCoder2 15B 15B Q8_0 17 GB CPU Offload ~17 tok/s
Devstral 24B 24B Q4_K_M 17 GB CPU Offload ~17 tok/s
Magistral Small 24B 24B Q4_K_M 17 GB CPU Offload ~17 tok/s
Mistral Small 3.1 24B 24B Q4_K_M 18 GB CPU Offload ~16 tok/s
Gemma 2 27B 27B Q4_K_M 17.7 GB CPU Offload ~16 tok/s
Gemma 3 27B 27B Q4_K_M 20 GB CPU Offload ~14 tok/s
Qwen 3 30B-A3B (MoE) 30B Q4_K_M 22 GB CPU Offload ~13 tok/s
Cogito 32B 32B Q4_K_M 21.5 GB CPU Offload ~14 tok/s
DeepSeek R1 32B 32B Q4_K_M 20.7 GB CPU Offload ~14 tok/s
Qwen 2.5 32B 32B Q4_K_M 20.7 GB CPU Offload ~14 tok/s
Qwen 2.5 Coder 32B 32B Q4_K_M 23 GB CPU Offload ~13 tok/s
Qwen 3 32B 32B Q4_K_M 23 GB CPU Offload ~13 tok/s
QwQ 32B 32B Q4_K_M 21.5 GB CPU Offload ~14 tok/s
Command R 35B 35B Q4_K_M 22.5 GB CPU Offload ~13 tok/s
20 model(s) are too large for this hardware.