NVIDIA GeForce RTX 5060 Ti 16GB 16GB GPU memory, main unit cost separate
448 GB/s GDDR7 VRAM. 16GB entry desktop GPU with 8B and 12B Q4 models 100% loadable in dedicated VRAM.
Speed and memory for the same model
Hardware core specifications
- Available memory
- 15GB
- Memory bandwidth
- 448GB/s
- Estimated sustained LLM load
- About 250W
- Korean price range (KRW)
- 99 × KRW 10,000–108 × KRW 10,000
Who it fits
- Utilizes 15GB of available memory
- CUDA-based local inference
- K2 Horizon MoVA 36B-A4B-class high-speed generation
Skip it if
Prices cover the GPU alone; electricity estimates cover the entire PC including the GPU. Add the host PC, power supply and cooling to your purchase budget.
Recommended format for each device 4K input
The model you will experience with this equipment
K2 Horizon 0.9B
Required 1.03GB · spare time
llama.cpp CUDA · Q4_K_M
Decode
634 ~ 701.6 tok/s
First token
0.36 to 0.65 seconds
K2 Horizon MoVA 36B-A4B
recommendedRequired 21.94GB · tight
llama.cpp CUDA · Q4_K_M
Decode
17.4 ~ 17.6 tok/s
First token
2.1 to 3.8 seconds
Actual speed varies with runtime version, cooling, context length and quantization file.
On sales pages, look at memory before chips.
The model and speed assumptions above change if the product is not the 16GB configuration.
Frequently asked questions
- Which local LLM can be run on an RTX 5060 Ti 16GB?
- K2 Horizon 0.9B, K2 Horizon MoVA 36B-A4B and others with a 4K input and the recommended format. Required memory varies by model and context length.
- What is the power consumption of a local LLM on an RTX 5060 Ti 16GB?
- Sustained load is about 250 W, with an actual range of 190–320 W.
- Are the displayed token speeds ground truth?
- This is a range aligned to collected benchmark measurements under the same conditions. Combinations without a matching measurement are estimated from the nearest result and vary with runtime, cooling, quantization file and context length.
Equipment to compare together
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This is the right configuration to minimize costs and start with 8B to 12B models.
similar memory
Mac mini M4 24GB
If you summarize short documents and ask about small sections of code, first check whether the model you plan to use gives useful answers. If a 12B Q4 model does the job, 32GB is worth considering only when your work needs more memory headroom.
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If you own an RTX 3090, first check whether the target model and context fit in 24GB. Newer hardware alone is not a reason to replace a card that completes your work. For a used purchase, include power, cooling, case compatibility and return terms in the cost.