NVIDIA RTX 4080 vs NVIDIA RTX 3080 10GB
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 4080 wins for local AI inference. It has 6 GB more VRAM and -6% more memory bandwidth, runs 41 models natively (vs 25), and exclusively fits 16 models the other cannot.
Specs comparison
| Spec | NVIDIA RTX 4080 | NVIDIA RTX 3080 10GB |
|---|---|---|
| VRAM | 16 GB | 10 GB |
| Memory type | GDDR6X | GDDR6X |
| Bandwidth | 717 GB/s | 760 GB/s(+6%) |
| Architecture | Ada Lovelace | Ampere |
| Backend | CUDA | CUDA |
| Tier | Consumer | Consumer |
| Released | 2022 | 2020 |
| Models (native) | 41 | 25 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX 4080 | NVIDIA RTX 3080 10GB | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | — | — | — |
| Qwen 3.6 27B(27B) | 66.4 t/s(Q3_K_M) | — | — |
| Llama 3.1 8B Instruct(8B) | 89.6 t/s(Q8) | 126.7 t/s(Q6_K) | -29% |
| Qwen 2.5 7B Instruct(7.6B) | 94.3 t/s(Q8) | 100 t/s(Q8) | -6% |
Delta is NVIDIA RTX 4080 relative to NVIDIA RTX 3080 10GB.
Only NVIDIA RTX 4080 can run(16)
Only NVIDIA RTX 3080 10GB can run(0)
No exclusive models — NVIDIA RTX 4080 can run everything NVIDIA RTX 3080 10GB can.
Both run natively(25)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- GPT-OSS 20B315.5 t/svs696.7 t/s
- Qwen3 14B64.6 t/svs128.4 t/s
- Qwen 2.5 14B Instruct65 t/svs129.3 t/s
- Phi-4 14B Instruct68.3 t/svs108.6 t/s
- Mistral Nemo 12B Instruct58.8 t/svs124.6 t/s
- Gemma 3 12B Instruct58.8 t/svs124.6 t/s
- Gemma 2 9B Instruct77.9 t/svs165.2 t/s
- Llama 3.1 8B Instruct89.6 t/svs126.7 t/s
- DeepSeek R1 Distill Llama 8B89.6 t/svs126.7 t/s
- Qwen3 8B89.6 t/svs126.7 t/s
- Qwen 2.5 7B Instruct94.3 t/svs100 t/s
- Mistral 7B Instruct v0.398.9 t/svs104.8 t/s
- Gemma 3 4B Instruct89.6 t/svs190 t/s
- Gemma 4 E4B89.6 t/svs190 t/s
- Phi-3.5 Mini Instruct94.3 t/svs200 t/s
- Llama 3.2 3B Instruct112 t/svs118.8 t/s
- +9 more on both
Which should you choose?
Choose NVIDIA RTX 4080 if:
- • You need to run larger models (>10 GB VRAM)
- • You want the newer architecture and longer driver support lifecycle
Choose NVIDIA RTX 3080 10GB if:
- • Faster token generation is the priority
Frequently asked questions
- Which is better for local AI, the NVIDIA RTX 4080 or NVIDIA RTX 3080 10GB?
- For local AI inference, the NVIDIA RTX 4080 has the edge. It offers 16 GB VRAM (vs 10 GB) and 717 GB/s bandwidth (vs 760 GB/s), letting it run 41 models natively in VRAM vs 25 for its rival.
- How much VRAM does the NVIDIA RTX 4080 have vs the NVIDIA RTX 3080 10GB?
- The NVIDIA RTX 4080 has 16 GB of GDDR6X at 717 GB/s. The NVIDIA RTX 3080 10GB has 10 GB of GDDR6X at 760 GB/s. The NVIDIA RTX 4080 has 6 GB more VRAM, allowing it to run 16 models the NVIDIA RTX 3080 10GB cannot fit natively.
- 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.
- Can the NVIDIA RTX 3080 10GB run Llama 3.3 70B?
- The NVIDIA RTX 3080 10GB 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 4080 and NVIDIA RTX 3080 10GB for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX 4080 has 16 GB VRAM at 717 GB/s (CUDA backend). The NVIDIA RTX 3080 10GB has 10 GB VRAM at 760 GB/s (CUDA backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX 4080 runs 41 models natively vs 25 for the NVIDIA RTX 3080 10GB.