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 99 open-weight models.
Quick verdict
NVIDIA RTX 4080 wins for local AI inference. It has 6 GB more VRAM and -5.7% more memory bandwidth, runs 46 models natively (vs 30), and exclusively fits 16 models the other cannot.
Analysis
The RTX 3080 and RTX 4080 are separated by two years, one architecture generation, and $500 in launch price ($699 vs $1,199), but VRAM capacity, not raw speed, is what actually separates them for local LLM work. The RTX 3080's wider memory bus gives it slightly more raw bandwidth than the newer, pricier card, a genuine surprise this site's calculator confirms directly.
NVIDIA's Ampere-generation RTX 3080 pairs 10GB of GDDR6X with a 320-bit bus for 760 GB/s; the RTX 4080's Ada Lovelace AD103 die runs a narrower 256-bit bus at 717 GB/s, 6.0% less bandwidth despite two years of architecture progress and 60% more VRAM (16GB vs 10GB). Since decode speed is bandwidth-bound, that gap shows up directly for any model both cards fit: Llama 3.1 8B at its recommended Q5_K_M decodes at 83.0 tok/s on the RTX 3080 versus 79.1 tok/s on the RTX 4080, and Qwen 2.5 7B at Q4_K_M reaches 107.5 tok/s versus 102.5 tok/s, both a real edge of roughly 5% for the older, cheaper card, just under its 6% bandwidth advantage. What the RTX 3080 can't do is hold larger models at all: Qwen3 14B at its recommended Q5_K_M needs 13.31 GB, comfortably inside the RTX 4080's usable VRAM (47.1 tok/s) but past the RTX 3080's 10GB ceiling, dropping to a CPU-offloaded 8.9 tok/s. GPT-OSS 20B, a model OpenAI explicitly sized to fit a 16GB card, shows the same pattern: 173.4 tok/s native on the RTX 4080 versus 43.8 tok/s offloaded on the RTX 3080 (a mixture-of-experts model streams only its active experts from system RAM, so it degrades far more gracefully than a dense one), about four times slower for lack of 6GB.
Bottom line: For anyone whose models genuinely fit in 10GB, the used-market RTX 3080 is not just cheaper than the RTX 4080, it's a few percent faster too, since its wider bus gives it slightly more raw bandwidth. But that 10GB ceiling is the real constraint: it locks out 14B-class models and anything larger entirely, where the RTX 4080's extra 6GB is the difference between a native fit and a heavily offloaded fraction of the speed. Anyone planning to run 13B+ models locally should treat the RTX 4080's VRAM, not its slightly lower bandwidth, as the deciding factor.
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) | 46 | 30 |
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) | N/A | N/A | N/A |
| Qwen 3.6 27B(27B) | 39.6 t/s(Q3_K_M) | N/A | N/A |
| Llama 3.1 8B Instruct(8B) | 58.5 t/s(Q8_0) | 74.9 t/s(Q6_K) | -5%at Q6_K |
| Qwen 2.5 7B Instruct(7.6B) | 65.7 t/s(Q8_0) | 85.4 t/s(Q6_K) | -5%at Q6_K |
Delta is NVIDIA RTX 4080 relative to NVIDIA RTX 3080 10GB, compared at the highest quantization that fits natively on both cards.
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(30)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Bonsai 27B62.1 t/svs64.9 t/s
- Bonsai 2 27B71.2 t/svs74.3 t/s
- Qwen3 14B42 t/svs66.4 t/s
- Qwen 2.5 14B Instruct45.4 t/svs73.2 t/s
- Phi-4 14B Instruct43 t/svs66.8 t/s
- Mistral Nemo 12B Instruct49 t/svs75.9 t/s
- Gemma 3 12B Instruct49.8 t/svs77.7 t/s
- Gemma 4 12B (Unified)41.9 t/svs68.9 t/s
- Gemma 2 9B Instruct44.5 t/svs66.2 t/s
- Qwen 3.5 9B57.1 t/svs74.8 t/s
- Ornith 1.5 9B57.1 t/svs74.8 t/s
- Llama 3.1 8B Instruct58.5 t/svs74.9 t/s
- DeepSeek R1 Distill Llama 8B58.5 t/svs74.9 t/s
- Qwen3 8B57 t/svs72.5 t/s
- Qwen 2.5 7B Instruct65.7 t/svs85.4 t/s
- Gemma 4 E4B96.6 t/svs101 t/s
- +14 more on both
Which should you choose?
- • You need to run larger models (>10 GB VRAM)
- • You want the newer architecture and longer driver support lifecycle
- • 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 46 models natively in VRAM vs 30 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 Q2_K, 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 46 models natively vs 30 for the NVIDIA RTX 3080 10GB.