NVIDIA RTX 4080 Super vs NVIDIA RTX 3080
Which graphics card is better for running local language models? Here is how the NVIDIA RTX 4080 Super (16 GB) compares against the NVIDIA RTX 3080 (10 GB) in real memory headroom, supported model architectures, and generation speed.
NVIDIA RTX 4080 Super
- Memory Bandwidth: 736 GB/s (GDDR6X 256-bit)
- Fits fully in VRAM: 34 popular models
- Power Draw (TDP): 320W
- Largest recommended (Q4): 20B–22B
NVIDIA RTX 3080
- Memory Bandwidth: 760 GB/s (GDDR6X 320-bit)
- Fits fully in VRAM: 29 popular models
- Power Draw (TDP): 320W
- Largest recommended (Q4): 8B
The AI Local Check Verdict: Which GPU should you buy for AI?
The Classic Headroom vs Speed Trade-off: The NVIDIA RTX 4080 Super has 6 GB more VRAM, allowing it to fit larger models that cannot run locally on the NVIDIA RTX 3080.
However, the NVIDIA RTX 3080 has a higher memory bandwidth (760 GB/s vs 736 GB/s). For models that comfortably fit in 10 GB VRAM, the NVIDIA RTX 3080 will generate tokens roughly 3.0% faster. Choose the NVIDIA RTX 4080 Super if you prioritize model size flexibility, or the NVIDIA RTX 3080 if you prioritize token generation speed on models up to 10 GB.
Models that fit on NVIDIA RTX 4080 Super, but NOT on NVIDIA RTX 3080
These models run entirely in GPU VRAM on the NVIDIA RTX 4080 Super, but require slow system RAM offload on the NVIDIA RTX 3080:
| Model | Size | Downloads | NVIDIA RTX 4080 Super | NVIDIA RTX 3080 |
|---|---|---|---|---|
| unsloth/gpt-oss-20b-GGUF | 20.91B | 501,909 | Fits in VRAM | Offload (Slow) |
| unsloth/Qwen-AgentWorld-35B-A3B-GGUF | 34.66B | 373,970 | Fits in VRAM | Offload (Slow) |
| MaziyarPanahi/Qwen3-30B-A3B-GGUF | 30.53B | 260,445 | Fits in VRAM | Offload (Slow) |
| MaziyarPanahi/Qwen3-32B-GGUF | 32.76B | 259,844 | Fits in VRAM | Offload (Slow) |
| bartowski/Qwen2.5-32B-Instruct-GGUF | 32.76B | 216,296 | Fits in VRAM | Offload (Slow) |