Best AI Models for NVIDIA GeForce RTX 5060 Ti 16GB (16.0GB)
16 GB is a comfortable mid-range tier for local AI. Most 7B–13B models run smoothly at good quantization levels, and smaller models can run at near-full precision.
This memory tier strikes a nice balance between price and capability. Popular 7B models like Llama 3 8B, Mistral 7B, and Qwen 2.5 7B all run very well at Q4_K_M quantization with fast inference and reasonable context windows. You can also fit some larger 13B models at Q3–Q4, though you'll want to keep context lengths modest. Small models like Phi 3 Mini (3.8B) practically fly at Q8 or even FP16 quality.
Runs Well
- 7B models at Q4–Q6 quality with good speed
- Small models (3B–4B) at Q8 or FP16
- 9B models (Gemma 2 9B) at Q4_K_M
Challenging
- 13B–14B models need Q3 or lower
- 30B+ models do not fit in VRAM
- Long context (>8K tokens) with larger models
What LLMs Can NVIDIA GeForce RTX 5060 Ti 16GB Run?
92 models · 15 good
Showing compatibility for NVIDIA GeForce RTX 5060 Ti 16GB
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·71.5 t/s tok/s·4K ctx·EASY RUN | Q4_K_M | 4.1 GB | 71.5 t/s | 4K | EASY RUN | C40 |
Q4_K_M·110.3 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 2.6 GB | 110.3 t/s | 2K | EASY RUN | C34 |
Q4_K_M·137.4 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.1 GB | 137.4 t/s | 131K | EASY RUN | C32 |
Q4_K_M·130.6 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 2.2 GB | 130.6 t/s | 33K | EASY RUN | C32 |
Q4_K_M·126.6 t/s tok/s·66K ctx·EASY RUN | Q4_K_M | 2.3 GB | 126.6 t/s | 66K | EASY RUN | C32 |
Q4_K_M·355.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 0.8 GB | 355.1 t/s | 131K | EASY RUN | D28 |
Q4_K_M·101.5 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.9 GB | 101.5 t/s | 131K | EASY RUN | C34 |
Q4_K_M·116.0 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.5 GB | 116.0 t/s | 131K | EASY RUN | C33 |
Q4_K_M·168.3 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 1.7 GB | 168.3 t/s | 8K | EASY RUN | C31 |
Q4_K_M·288.3 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 1.0 GB | 288.3 t/s | 2K | EASY RUN | D28 |
Q4_K_M·133.6 t/s tok/s·16K ctx·EASY RUN | Q4_K_M | 2.2 GB | 133.6 t/s | 16K | EASY RUN | C32 |
Q4_K_M·441.2 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 0.7 GB | 441.2 t/s | 33K | EASY RUN | D27 |
IQ2_M·19.8 t/s tok/s·131K ctx·POOR FIT | IQ2_M | 14.7 GB | 19.8 t/s | 131K | POOR FIT | C44 |
Q4_K_M·1617.8 t/s tok/s·EASY RUN | Q4_K_M | 0.2 GB | 1617.8 t/s | — | EASY RUN | D26 |
Q3_K_S·19.5 t/s tok/s·41K ctx·POOR FIT | Q3_K_S | 15.0 GB | 19.5 t/s | 41K | POOR FIT | C36 |
Q4_K_M·240.7 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 1.2 GB | 240.7 t/s | 8K | EASY RUN | D29 |
NVIDIA GeForce RTX 5060 Ti 16GB Specifications
- Brand
- NVIDIA
- Architecture
- Blackwell
- Compute Capability
- 12.0 (CUDA SM version)
- VRAM
- 16.0 GB GDDR7
- Memory Bandwidth
- 448.0 GB/s
- CUDA Cores
- 4,608
- Tensor Cores
- 144
- FP16 Performance
- 47.50 TFLOPS
- TDP
- 180W
- Release Date
- 2025-04-16
- MSRP
- $429
Get Started
GPUs to Consider Over NVIDIA GeForce RTX 5060 Ti 16GB
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA GeForce RTX 5090
NVIDIA · Blackwell
NVIDIA GeForce RTX 3090 Ti
NVIDIA · Ampere
NVIDIA GeForce RTX 4090
NVIDIA · Ada Lovelace
AMD Radeon RX 7900 XTX
AMD · RDNA 3
NVIDIA GeForce RTX 5080
NVIDIA · Blackwell
NVIDIA GeForce RTX 3090
NVIDIA · Ampere
Frequently Asked Questions
- Can NVIDIA GeForce RTX 5060 Ti 16GB run Phi 4?
Yes, the NVIDIA GeForce RTX 5060 Ti 16GB with 16 GB can run Phi 4, GPT OSS 20B, Qwen1.5 14B, and 1192 other models. 4 models run at excellent quality, and 178 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is NVIDIA GeForce RTX 5060 Ti 16GB good for AI?
The NVIDIA GeForce RTX 5060 Ti 16GB has 16 GB of GDDR7, making it very good for running local AI models. It supports 182 models at good quality or better. With 448.0 GB/s memory bandwidth, it delivers solid token generation speeds. This is a solid mid-range card for running 7B–14B parameter models at good quality.
- How many parameters can NVIDIA GeForce RTX 5060 Ti 16GB handle?
With 16 GB, the NVIDIA GeForce RTX 5060 Ti 16GB supports models from 3B to 14B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 26B parameters. 7B models run at high quality (Q5/Q6), while 14B models fit comfortably at Q4.
- What quantization should I use on NVIDIA GeForce RTX 5060 Ti 16GB?
For the best balance of quality and speed on the NVIDIA GeForce RTX 5060 Ti 16GB, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. You can step up to Q5_K_M for 7B models to get better quality. For 14B models that just barely fit, Q4_K_M is ideal.
- How fast is NVIDIA GeForce RTX 5060 Ti 16GB for AI inference?
With 448.0 GB/s memory bandwidth, the NVIDIA GeForce RTX 5060 Ti 16GB achieves approximately 65 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~32 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (448 GB/s ÷ model GB) × efficiency
Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.
Estimated speed on NVIDIA GeForce RTX 5060 Ti 16GB
~31 tok/s~22 tok/s~28 tok/s~36 tok/sReal-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.
- What's the best model for NVIDIA GeForce RTX 5060 Ti 16GB?
The top-rated models for the NVIDIA GeForce RTX 5060 Ti 16GB are Phi 4, GPT OSS 20B, Qwen1.5 14B. The best choice depends on your use case: coding assistants benefit from code-tuned models, while general chat works well with instruction-tuned models like Llama or Qwen.
- What power supply and cooling does NVIDIA GeForce RTX 5060 Ti 16GB need?
The NVIDIA GeForce RTX 5060 Ti 16GB has a TDP of 180 W. A good rule of thumb is to provide at least double the GPU's TDP to cover the rest of the system — that means a 550 W PSU or larger. A mid-tower case with one intake and one rear exhaust is usually sufficient. Keep dust filters clean, as sustained inference generates continuous heat rather than the brief spikes typical of gaming.