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NVIDIA GeForce RTX 5060 Ti 8GB

NVIDIA · 8GBGDDR7 · Can run 57 models

BuyAmazon
ManufacturerNVIDIA
VRAM8 GB
Memory TypeGDDR7
ArchitectureBlackwell
CUDA Cores4,608
Tensor Cores144
Bandwidth448 GB/s
TDP180W
MSRP$379
ReleasedApr 16, 2025

AI Notes

The RTX 5060 Ti 8GB offers Blackwell architecture at an accessible price, but its 8GB VRAM is the main constraint for AI workloads. It can run 7B-parameter models at Q4 quantization comfortably, though larger models will require heavy quantization or CPU offloading. Best suited for smaller models and experimentation.

Compatible Models

ModelParametersBest QuantVRAM UsedFitEst. Speed
Qwen 3 0.6B600MQ4_K_M2.5 GBRuns~179 tok/s
Qwen 3.5 0.8B800MQ4_K_M1.5 GBRuns~299 tok/s
Gemma 3 1B1BQ8_02 GBRuns~224 tok/s
Llama 3.2 1B1BQ8_03 GBRuns~149 tok/s
DeepSeek R1 1.5B1.5BQ8_03 GBRuns~149 tok/s
SmolLM2 1.7B1.7BQ8_02.7 GBRuns~166 tok/s
Gemma 2 2B2BQ8_04 GBRuns~112 tok/s
Gemma 3n E2B2BQ4_K_M3.3 GBRuns~136 tok/s
Gemma 4 E2B2BQ4_K_M4 GBRuns~112 tok/s
Qwen 3.5 2B2BQ4_K_M3 GBRuns~149 tok/s
Llama 3.2 3B3BQ8_05 GBRuns~90 tok/s
StarCoder2 3B3BQ4_K_M3.5 GBRuns~128 tok/s
Phi-3 Mini 3.8B3.8BQ8_05.8 GBRuns~77 tok/s
Phi-4 Mini 3.8B3.8BQ4_K_M4.5 GBRuns~100 tok/s
Gemma 3 4B4BQ4_K_M5 GBRuns~90 tok/s
Gemma 3n E4B4BQ4_K_M4.5 GBRuns~100 tok/s
Gemma 4 E4B4BQ4_K_M6 GBRuns~75 tok/s
Qwen 3 4B4BQ4_K_M4.5 GBRuns~100 tok/s
Qwen 3.5 4B4BQ4_K_M4.5 GBRuns~100 tok/s
Yi 1.5 6B6BQ4_K_M5 GBRuns~90 tok/s
Falcon 3 7B7BQ4_K_M6.8 GBRuns~66 tok/s
InternLM 2.5 7B7BQ4_K_M5.5 GBRuns~81 tok/s
StarCoder2 7B7BQ4_K_M5.5 GBRuns~81 tok/s
Aya Expanse 8B8BQ4_K_M6.5 GBRuns~69 tok/s
Dolphin 3 8B8BQ4_K_M6 GBRuns~75 tok/s
Nous Hermes 2 8B8BQ4_K_M6 GBRuns~75 tok/s
Yi 1.5 9B9BQ4_K_M6.5 GBRuns~69 tok/s
Codestral Mamba 7B7BQ4_K_M6.9 GBRuns (tight)~65 tok/s
OpenChat 3.5 7B7BQ4_K_M6.9 GBRuns (tight)~65 tok/s
Qwen 2.5 VL 7B7BQ4_K_M7 GBRuns (tight)~64 tok/s
WizardLM 2 7B7BQ4_K_M6.9 GBRuns (tight)~65 tok/s
Cogito 8B8BQ4_K_M7.5 GBRuns (tight)~60 tok/s
DeepSeek R1 8B8BQ4_K_M7.5 GBRuns (tight)~60 tok/s
Nemotron 3 Nano 8B8BQ4_K_M7.5 GBRuns (tight)~60 tok/s
Qwen 3 8B8BQ4_K_M7.5 GBRuns (tight)~60 tok/s
Qwen 3.5 9B9BQ4_K_M7.5 GBRuns (tight)~60 tok/s
DeepSeek R1 7B7BQ8_09 GBCPU Offload~15 tok/s
Mistral 7B7BQ8_09 GBCPU Offload~15 tok/s
Qwen 2.5 7B7BQ8_09 GBCPU Offload~15 tok/s
Qwen 2.5 Coder 7B7BQ8_09 GBCPU Offload~15 tok/s
Granite 3.3 8B8BQ8_010 GBCPU Offload~14 tok/s
Llama 3.1 8B8BQ8_010 GBCPU Offload~14 tok/s
Gemma 2 9B9BQ8_011 GBCPU Offload~12 tok/s
Yi Coder 9B9BQ4_K_M8 GBCPU Offload~17 tok/s
Falcon 3 10B10BQ4_K_M8.5 GBCPU Offload~16 tok/s
Llama 3.2 Vision 11B11BQ4_K_M8.5 GBCPU Offload~16 tok/s
Gemma 3 12B12BQ4_K_M10.5 GBCPU Offload~13 tok/s
Mistral Nemo 12B12BQ4_K_M9.5 GBCPU Offload~14 tok/s
DeepSeek R1 14B14BQ4_K_M9.9 GBCPU Offload~14 tok/s
Phi-4 14B14BQ4_K_M9.9 GBCPU Offload~14 tok/s
Phi-4 Reasoning 14B14BQ4_K_M11 GBCPU Offload~12 tok/s
Qwen 2.5 14B14BQ4_K_M9.9 GBCPU Offload~14 tok/s
Qwen 2.5 Coder 14B14BQ4_K_M12 GBCPU Offload~11 tok/s
Qwen 3 14B14BQ4_K_M12 GBCPU Offload~11 tok/s
StarCoder2 15B15BQ4_K_M10.5 GBCPU Offload~13 tok/s
InternLM 2.5 20B20BQ4_K_M12 GBCPU Offload~11 tok/s
Qwen 3.5 35B A3B35BQ4_K_M12 GBCPU Offload~11 tok/s
57 model(s) are too large for this hardware.