NVIDIA · Blackwell · 2025
NVIDIA GeForce RTX 5060 Ti 16GB for local AI
The cheapest new way to get 16GB of CUDA memory, and a strong first LLM card.
Updated · Speeds marked ~ are estimates; seemethodology
- VRAM
- 16GB
- Bandwidth
- 448 GB/s
- Power
- 180W
- Runs well
- 6 models
RTX 5060 Ti 16GB
Typically about $620
Can the RTX 5060 Ti 16GB run LLMs?
The RTX 5060 Ti 16GB has 16GB of GDDR7 and 448 GB/s of memory bandwidth, which decides how big a model fits and how fast it generates text. It comfortably runs models up to about 15B parameters: the largest in our catalog is Phi-4 at Q5_K_M (13GB with 8K context, ~33 tokens/sec). Our top all-round pick for it is Qwen3.8 27B (Q3_K_M, ~26 tokens/sec). Llama 3.3 70B Instruct only runs with partial CPU offload, at about 1.4 tokens/sec with CPU offload.
Recommendations
Best models for the RTX 5060 Ti 16GB
Best overall · also coding, reasoning, vision, largest that fits
Qwen3.8 27BQ3_K_M · 15GB · ~26 tokens/sec
Fits, tightRunner-up
Qwen3.5 9BQ8_0 · 12GB · ~32 tokens/sec
Runs wellFastest good model
Gemma 4 26B A4BQ3_K_M · 14GB · ~90 tokens/sec
Fits, tight
Compatibility
What runs on the RTX 5060 Ti 16GB
Every current model in our catalog, at the best quantization that fits in 16GB with 8K tokens of context. "Partial offload" means part of the model runs from system RAM (we assume 64GB), which is much slower.
| Model | Parameters | Best quant | Memory | Speed | Verdict |
|---|---|---|---|---|---|
| Gemma 4 E4B | 8B | Q8_0 | 10 GB | ~36 tok/s | Runs well |
| Qwen3.5 9B | 9B | Q8_0 | 12 GB | ~32 tok/s | Runs well |
| Phi-4 | 14.7B | Q5_K_M | 13 GB | ~33 tok/s | Runs well |
| gpt-oss-20b | 20.9B (3.6B active) | MXFP4 | 15 GB | ~74 tok/s | Fits, tight |
| Gemma 4 26B A4B | 26B (3.8B active) | Q3_K_M | 14 GB | ~90 tok/s | Fits, tight |
| Qwen3.8 27B | 27B | Q3_K_M | 15 GB | ~26 tok/s | Fits, tight |
| Gemma 4 31B | 31B | Q4_K_M | 22 GB | ~5.2 tok/s | Partial offload |
| Qwen3.6 35B-A3B | 35B (3B active) | Q4_K_M | 24 GB | ~42 tok/s | Partial offload |
| GLM-4.5-Air | 106B (12B active) | — | — | — | Won't fitWhat it needs |
| Llama 4 Scout | 109B (17B active) | — | — | — | Won't fitWhat it needs |
| gpt-oss-120b | 117B (5.1B active) | — | — | — | Won't fitWhat it needs |
| Qwen3.5 122B-A10B | 122B (10B active) | — | — | — | Won't fitWhat it needs |
| DeepSeek V4 Flash | 284B (13B active) | — | — | — | Won't fitWhat it needs |
| GLM-5.3-Flash | 320B (18B active) | — | — | — | Won't fitWhat it needs |
| GLM-5.3 | 744B (40B active) | — | — | — | Won't fitWhat it needs |
Performance
RTX 5060 Ti 16GB vs. alternatives on Qwen3.8 27B
Generation speed in tokens per second for a single user. Token generation is limited mostly by memory bandwidth, which is why bandwidth predicts these results so well.
RTX 5060 Ti 16GB
Q3_K_M · 16GB
~26tok/s
RTX 4060 Ti 16GB
Q3_K_M · 16GB
~17tok/s
RX 9060 XT 16GB
Q3_K_M · 16GB
~15tok/s
Measured
RTX 5060 Ti 16GB benchmark results
| Model | Quant | Runtime | Generation | Prompt | Source |
|---|---|---|---|---|---|
| Llama 2 7B Chat | Q4_0 | llama.cpp (Vulkan) | 94 tok/s | 3460 tok/s | llama.cpp discussion #10879: Performance of llama.cpp with Vulkan · October 2026 |
| Llama 2 7B Chat | Q4_0 | llama.cpp (CUDA) | 91 tok/s | 3735 tok/s | llama.cpp discussion #15013: Performance of llama.cpp on NVIDIA CUDA · October 2026 |
Our take
Should you buy the RTX 5060 Ti 16GB for AI?
The RTX 5060 Ti 16GB pairs 16GB of GDDR7 with a 128-bit bus for 448 GB/s, about 55 percent more bandwidth than the RTX 4060 Ti 16GB it replaces. It runs 14B models at 8 bit and 24B models at 4 bit at usable speeds, and it draws only 180W. The card uses a PCIe 5.0 x8 link, which matters little for single-GPU inference but can slow multi-GPU setups on older PCIe 3.0 boards. Make sure you buy the 16GB version, because the 8GB model shares the name and is far less useful for AI.
What we like
- 16GB of VRAM at the lowest new price for NVIDIA
- 448 GB/s GDDR7 is a big step up from the RTX 4060 Ti
- 180W board power works with modest power supplies
- Full CUDA support
Watch out for
- 128-bit bus limits speed on larger models
- PCIe x8 link slows down on PCIe 3.0 systems
- Easy to confuse with the 8GB version
Best for: First local AI builds on a budget · Upgrading an older gaming PC for 8B to 24B models · Dual-card builds with 32GB total
Specifications
RTX 5060 Ti 16GB specs that matter for AI
- Memory
- 16GB GDDR7
- Memory bandwidth
- 448 GB/s
- Memory bus
- 128-bit
- Architecture
- Blackwell
- Power
- 180W
- Interface
- PCIe 5.0 x8
- Released
- 2025
- Launch price
- $429
- Street price (new)
- ~$620 (2026-10)
- Software
- CUDA, Vulkan
Sources: NVIDIA GeForce RTX 5060 family product page, NVIDIA: Ultimate guide to the RTX 5060 and 5060 Ti
Builds
PC builds with the RTX 5060 Ti 16GB
Cross-shop
Alternatives to the RTX 5060 Ti 16GB
Frequently asked questions
How much VRAM does the RTX 5060 Ti 16GB have?
The NVIDIA GeForce RTX 5060 Ti 16GB has 16GB of GDDR7 with 448 GB/s of memory bandwidth on a 128-bit bus.
What is the largest LLM the RTX 5060 Ti 16GB can run?
Fully in GPU memory, the largest model in our catalog that runs comfortably is Phi-4 (14.7B) at Q5_K_M, using about 13GB with 8K context at ~33 tokens/sec.
Can the RTX 5060 Ti 16GB run Llama 3.3 70B?
Only with partial CPU offload. At Q4_K_M the model needs about 47GB with 8K context, so part of it spills into system RAM and speed drops to about 1.4 tokens/sec with CPU offload. For usable 70B speeds you want 48GB of VRAM or more (for example two 24GB cards).
What is the best LLM to run on the RTX 5060 Ti 16GB?
Our top pick is Qwen3.8 27B at Q3_K_M, which fits in 16GB with room for 8K of context and runs at ~26 tokens/sec. See our full list of the best models for the RTX 5060 Ti 16GB for coding, reasoning, and vision picks.
Is the RTX 5060 Ti 16GB good for local AI in 2026?
The cheapest new way to get 16GB of CUDA memory, and a strong first LLM card.