NVIDIA RTX 3090 vs NVIDIA RTX 5080
Which graphics card is better for running local language models? Here is how the NVIDIA RTX 3090 (24 GB) compares against the NVIDIA RTX 5080 (16 GB) in real memory headroom, supported model architectures, and generation speed.
NVIDIA RTX 3090
- Memory Bandwidth: 936 GB/s (GDDR6X 384-bit)
- Fits fully in VRAM: 37 popular models
- Power Draw (TDP): 350W
- Largest recommended (Q4): 32B–34B
NVIDIA RTX 5080
- Memory Bandwidth: 960 GB/s (GDDR7 256-bit)
- Fits fully in VRAM: 35 popular models
- Power Draw (TDP): 400W
- Largest recommended (Q4): 20B–22B
The AI Local Check Verdict: Which GPU should you buy for AI?
The Classic Headroom vs Speed Trade-off: The NVIDIA RTX 3090 has 8 GB more VRAM, allowing it to fit larger models that cannot run locally on the NVIDIA RTX 5080.
However, the NVIDIA RTX 5080 has a higher memory bandwidth (960 GB/s vs 936 GB/s). For models that comfortably fit in 16 GB VRAM, the NVIDIA RTX 5080 will generate tokens roughly 3.0% faster. Choose the NVIDIA RTX 3090 if you prioritize model size flexibility, or the NVIDIA RTX 5080 if you prioritize token generation speed on models up to 16 GB.
Models that fit on NVIDIA RTX 3090, but NOT on NVIDIA RTX 5080
These models run entirely in GPU VRAM on the NVIDIA RTX 3090, but require slow system RAM offload on the NVIDIA RTX 5080:
| Model | Size | Downloads | NVIDIA RTX 3090 | NVIDIA RTX 5080 |
|---|---|---|---|---|
| ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF | 31.58B | 180,114 | Fits in VRAM | Offload (Slow) |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | 138,575 | Fits in VRAM | Offload (Slow) |