Local AI suitability
The RTX 4080 pairs 16 GB of fast GDDR6X with 717 GB/s of bandwidth, making it a strong premium consumer card for local inference. It runs 14B models at comfortable quantization, 32B at tight quantization, and everything smaller at high quality.
Excellent. 16 GB holds 32B models at Q3 and even Q4 with careful context management, and supports long-context work on 14B models. Speed is roughly double the 4070 class thanks to bandwidth.
Recommended model sizes
- 7B (FP16)
- 14B (Q6/Q8)
- 32B (Q3/Q4)
Recommended quantizations
- Q4_K_M / Q5_K_M (GGUF)
- GPTQ Int4
- AWQ 4-bit
Limitations
- 32B at Q4 uses nearly all of the 16 GB, leaving little context headroom
- Premium price; the 4090 (24 GB) is often the better pure-AI buy if budget allows
How it compares
The 4080 and 4070 Ti Super share 16 GB, but the 4080's extra bandwidth and cores make it meaningfully faster for inference. Against the 4090, the 8 GB VRAM difference matters more than speed for 32B+ ambitions.
Compatible models
Model pages below include per-quantization memory estimates and FAQs.
FAQ
Can an RTX 4080 run a 32B model?
Yes, at 4-bit quantization a 32B model needs roughly 17–20 GB including overhead — Q3 fits comfortably, Q4 is possible with small context. CPU offload of a few layers is a common compromise.
Is the 4080 worth it over a 4070 for AI?
If your target models are 14B or smaller, not necessarily — the 4070 already runs those well. If you want 32B-class or long-context reasoning models, the extra 4 GB and bandwidth are decisive.
How fast is inference on a 4080?
A Q4 14B model typically achieves 40–60 tok/s; Q4 32B with partial offload lands in the teens. Speeds vary with runtime, context and power settings.
Does it support AWQ and vLLM?
Yes — full CUDA support means vLLM with AWQ/GPTQ Int4 models works well, and 16 GB gives vLLM the headroom it needs for its runtime overhead.