NVIDIA GeForce RTX 4060 Ti 16GB
LLM Inference Performance
| Model | Tokens / sec | Local Fit |
|---|---|---|
| Mistral 7b Q4 | 58 tok/s | fits · single GPU |
| Llama 3 8b Q4 | 55 tok/s | fits · single GPU |
| Llama 3 13b Q4 | 30 tok/s | fits · single GPU |
| Llama 3 70b Q4 | — OOM — | OOM / offload |
Local Model Compatibility
Spec Sheet
Comparable GPUs
Analysis notes
Quick Summary
The RTX 4060 Ti 16GB AI story is a trade-off: lots of VRAM for very few watts, undercut by a narrow memory bus. At 16GB and 165W it fits bigger models in a small, efficient package — but 288 GB/s bandwidth means it generates tokens slower than the capacity implies.
Specs That Matter for AI
16GB GDDR6 is the draw, letting it hold 13B models and larger contexts comfortably. The catch is the 128-bit bus at 288 GB/s; LLM inference is memory-bandwidth-bound, so the card punches below its VRAM weight.
Performance
Around 55 tok/s on Llama 3 8B q4 — fine for interactive use, slow for heavy workloads. Its strength is fitting models, not racing through them.
Verdict
Buy it for capacity-at-low-power: quiet, efficient builds that need 16GB. For pure inference speed, a wider-bus card is the better value.
Frequently Asked Questions
- Why is the RTX 4060 Ti 16GB slower than its VRAM suggests?
- Its memory bus is only 128-bit, giving 288 GB/s — low for the class. Token generation is bandwidth-bound, so despite 16GB it runs LLMs slower than cards with narrower memory but wider buses.
- Is 16GB worth it on the 4060 Ti for AI?
- If you need to fit larger context or 13B+ models at low power, yes. If you want speed per dollar, a wider-bus card like the 4070 Super is a better inference performer.