NVIDIA GeForce RTX 5060 Ti 8GB
NVIDIA · 8GB GDDR7 · Can run 35 models
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| Manufacturer | NVIDIA |
| VRAM | 8 GB |
| Memory Type | GDDR7 |
| Architecture | Blackwell |
| CUDA Cores | 4,608 |
| Tensor Cores | 144 |
| Bandwidth | 448 GB/s |
| TDP | 180W |
| MSRP | $379 |
| Released | Apr 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
| Model | Parameters | Best Quant | VRAM Used | Fit | Est. Speed |
|---|---|---|---|---|---|
| Qwen 3 0.6B | 600M | Q4_K_M | 2.5 GB | Runs | ~179 tok/s |
| Gemma 3 1B | 1B | Q8_0 | 2 GB | Runs | ~224 tok/s |
| Llama 3.2 1B | 1B | Q8_0 | 3 GB | Runs | ~149 tok/s |
| DeepSeek R1 1.5B | 1.5B | Q8_0 | 3 GB | Runs | ~149 tok/s |
| Gemma 2 2B | 2B | Q8_0 | 4 GB | Runs | ~112 tok/s |
| Gemma 3n E2B | 2B | Q4_K_M | 3.3 GB | Runs | ~136 tok/s |
| Llama 3.2 3B | 3B | Q8_0 | 5 GB | Runs | ~90 tok/s |
| Phi-3 Mini 3.8B | 3.8B | Q8_0 | 5.8 GB | Runs | ~77 tok/s |
| Phi-4 Mini 3.8B | 3.8B | Q4_K_M | 4.5 GB | Runs | ~100 tok/s |
| Gemma 3 4B | 4B | Q4_K_M | 5 GB | Runs | ~90 tok/s |
| Gemma 3n E4B | 4B | Q4_K_M | 4.5 GB | Runs | ~100 tok/s |
| Qwen 3 4B | 4B | Q4_K_M | 4.5 GB | Runs | ~100 tok/s |
| Falcon 3 7B | 7B | Q4_K_M | 6.8 GB | Runs | ~66 tok/s |
| Qwen 2.5 VL 7B | 7B | Q4_K_M | 7 GB | Runs (tight) | ~64 tok/s |
| Cogito 8B | 8B | Q4_K_M | 7.5 GB | Runs (tight) | ~60 tok/s |
| DeepSeek R1 8B | 8B | Q4_K_M | 7.5 GB | Runs (tight) | ~60 tok/s |
| Nemotron 3 Nano 8B | 8B | Q4_K_M | 7.5 GB | Runs (tight) | ~60 tok/s |
| Qwen 3 8B | 8B | Q4_K_M | 7.5 GB | Runs (tight) | ~60 tok/s |
| DeepSeek R1 7B | 7B | Q8_0 | 9 GB | CPU Offload | ~15 tok/s |
| Mistral 7B | 7B | Q8_0 | 9 GB | CPU Offload | ~15 tok/s |
| Qwen 2.5 7B | 7B | Q8_0 | 9 GB | CPU Offload | ~15 tok/s |
| Qwen 2.5 Coder 7B | 7B | Q8_0 | 9 GB | CPU Offload | ~15 tok/s |
| Llama 3.1 8B | 8B | Q8_0 | 10 GB | CPU Offload | ~14 tok/s |
| Gemma 2 9B | 9B | Q8_0 | 11 GB | CPU Offload | ~12 tok/s |
| Falcon 3 10B | 10B | Q4_K_M | 8.5 GB | CPU Offload | ~16 tok/s |
| Llama 3.2 Vision 11B | 11B | Q4_K_M | 8.5 GB | CPU Offload | ~16 tok/s |
| Gemma 3 12B | 12B | Q4_K_M | 10.5 GB | CPU Offload | ~13 tok/s |
| Mistral Nemo 12B | 12B | Q4_K_M | 9.5 GB | CPU Offload | ~14 tok/s |
| DeepSeek R1 14B | 14B | Q4_K_M | 9.9 GB | CPU Offload | ~14 tok/s |
| Phi-4 14B | 14B | Q4_K_M | 9.9 GB | CPU Offload | ~14 tok/s |
| Phi-4 Reasoning 14B | 14B | Q4_K_M | 11 GB | CPU Offload | ~12 tok/s |
| Qwen 2.5 14B | 14B | Q4_K_M | 9.9 GB | CPU Offload | ~14 tok/s |
| Qwen 2.5 Coder 14B | 14B | Q4_K_M | 12 GB | CPU Offload | ~11 tok/s |
| Qwen 3 14B | 14B | Q4_K_M | 12 GB | CPU Offload | ~11 tok/s |
| StarCoder2 15B | 15B | Q4_K_M | 10.5 GB | CPU Offload | ~13 tok/s |
34
model(s) are too large for this hardware.