NVIDIA RTX 4080 Super vs NVIDIA RTX 4090
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 4090 (24 GB) in real memory headroom, supported model architectures, and generation speed.
NVIDIA · Ada Lovelace
16 GB VRAM
NVIDIA RTX 4080 Super
- Memory Bandwidth: 736 GB/s (GDDR6X 256-bit)
- Fits fully in VRAM: 31 popular models
- Power Draw (TDP): 320W
- Largest recommended (Q4): 20B–22B
NVIDIA · Ada Lovelace
24 GB VRAM
NVIDIA RTX 4090
- Memory Bandwidth: 1008 GB/s (GDDR6X 384-bit)
- Fits fully in VRAM: 36 popular models
- Power Draw (TDP): 450W
- Largest recommended (Q4): 32B–34B
The AI Local Check Verdict: Which GPU should you buy for AI?
The NVIDIA RTX 4090 is the clear winner for local AI. It delivers 8 GB more VRAM and superior memory bandwidth (1008 GB/s vs 736 GB/s).
It unlocks larger parameter classes without system RAM offloading and generates tokens noticeably faster.
Models that fit on NVIDIA RTX 4090, but NOT on NVIDIA RTX 4080 Super
These models run entirely in GPU VRAM on the NVIDIA RTX 4090, but require slow system RAM offload on the NVIDIA RTX 4080 Super:
| Model | Size | Downloads | NVIDIA RTX 4090 | NVIDIA RTX 4080 Super |
|---|---|---|---|---|
| ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF | 31.58B | 274,796 | Fits in VRAM | Offload (Slow) |
| bartowski/Qwen_Qwen3-Next-80B-A3B-Thinking-GGUF | 81.32B | 248,583 | Fits in VRAM | Offload (Slow) |
| unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF | 70.55B | 201,777 | Fits in VRAM | Offload (Slow) |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | 195,568 | Fits in VRAM | Offload (Slow) |
| bartowski/Hermes-3-Llama-3.1-70B-GGUF | 70.55B | 158,086 | Fits in VRAM | Offload (Slow) |
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