NVIDIA RTX 5090 vs NVIDIA RTX 4080
Side-by-side local AI comparison — VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 70 open-weight models.
Quick verdict
NVIDIA RTX 5090 wins for local AI inference. It has 16 GB more VRAM and 150% more memory bandwidth, runs 47 models natively (vs 41), and exclusively fits 6 models the other cannot.
Specs comparison
| Spec | NVIDIA RTX 5090 | NVIDIA RTX 4080 |
|---|---|---|
| VRAM | 32 GB | 16 GB |
| Memory type | GDDR7 | GDDR6X |
| Bandwidth | 1792 GB/s(+150%) | 717 GB/s |
| Architecture | Blackwell | Ada Lovelace |
| Backend | CUDA | CUDA |
| Tier | Consumer | Consumer |
| Released | 2025 | 2022 |
| Models (native) | 47 | 41 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX 5090 | NVIDIA RTX 4080 | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 85.3 t/s(Q2_K) | — | — |
| Qwen 3.6 27B(27B) | 88.5 t/s(Q6_K) | 66.4 t/s(Q3_K_M) | +33% |
| Llama 3.1 8B Instruct(8B) | 112 t/s(FP16) | 89.6 t/s(Q8) | +25% |
| Qwen 2.5 7B Instruct(7.6B) | 117.9 t/s(FP16) | 94.3 t/s(Q8) | +25% |
Delta is NVIDIA RTX 5090 relative to NVIDIA RTX 4080.
Only NVIDIA RTX 5090 can run(6)
Only NVIDIA RTX 4080 can run(0)
No exclusive models — NVIDIA RTX 5090 can run everything NVIDIA RTX 4080 can.
Both run natively(41)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 3.5 35B-A3B (MoE)876.1 t/svs876.3 t/s
- Qwen 3.6 35B81.9 t/svs68.3 t/s
- Yi 1.5 34B Chat83.3 t/svs69.5 t/s
- Qwen3 32B72.8 t/svs72.9 t/s
- Qwen 2.5 32B Instruct73.5 t/svs73.5 t/s
- Qwen 2.5 Coder 32B Instruct73.5 t/svs73.5 t/s
- DeepSeek R1 Distill Qwen 32B73.5 t/svs73.5 t/s
- Nemotron 3 Nano 30B876.1 t/svs657.2 t/s
- Gemma 4 31B77.1 t/svs77.1 t/s
- Qwen3 30B-A3B (MoE)876.1 t/svs657.2 t/s
- Gemma 2 27B Instruct87.8 t/svs87.9 t/s
- Gemma 3 27B Instruct88.5 t/svs66.4 t/s
- Qwen 3.6 27B88.5 t/svs66.4 t/s
- Gemma 4 26B (MoE)691.6 t/svs518.9 t/s
- Mistral Small 3.1 24B Instruct74.7 t/svs59.8 t/s
- Mistral Small 22B80.7 t/svs64.6 t/s
- +25 more on both
Which should you choose?
Choose NVIDIA RTX 5090 if:
- • You need to run larger models (>16 GB VRAM)
- • Faster token generation is the priority
- • You want the newer architecture and longer driver support lifecycle
Choose NVIDIA RTX 4080 if:
Frequently asked questions
- Which is better for local AI, the NVIDIA RTX 5090 or NVIDIA RTX 4080?
- For local AI inference, the NVIDIA RTX 5090 has the edge. It offers 32 GB VRAM (vs 16 GB) and 1792 GB/s bandwidth (vs 717 GB/s), letting it run 47 models natively in VRAM vs 41 for its rival.
- How much VRAM does the NVIDIA RTX 5090 have vs the NVIDIA RTX 4080?
- The NVIDIA RTX 5090 has 32 GB of GDDR7 at 1792 GB/s. The NVIDIA RTX 4080 has 16 GB of GDDR6X at 717 GB/s. The NVIDIA RTX 5090 has 16 GB more VRAM, allowing it to run 6 models the NVIDIA RTX 4080 cannot fit natively.
- Can the NVIDIA RTX 5090 run Llama 3.3 70B?
- Yes. The NVIDIA RTX 5090 runs Llama 3.3 70B natively at Q2_K quantization at approximately 85.3 tokens per second.
- Can the NVIDIA RTX 4080 run Llama 3.3 70B?
- The NVIDIA RTX 4080 can run Llama 3.3 70B with CPU offload at Q3_K_M, but at reduced speed.
- What is the difference between the NVIDIA RTX 5090 and NVIDIA RTX 4080 for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX 5090 has 32 GB VRAM at 1792 GB/s (CUDA backend). The NVIDIA RTX 4080 has 16 GB VRAM at 717 GB/s (CUDA backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX 5090 runs 47 models natively vs 41 for the NVIDIA RTX 4080.