NVIDIA GeForce RTX 4060 Ti 16GB
16 GB GDDR6 · 288 GB/s
From
$449
Estimated street price
VRAM
16 GB
Bandwidth
288 GB/s
TDP
165W
Models
35
Tier
Capable
The NVIDIA GeForce RTX 4060 Ti 16GB with 16 GB GDDR6 VRAM can handle 35 AI models across coding, ai_coding, ai_building. Best performance: Llama 3.2 1B Instruct at 120 tok/s (excellent). For AI coding workflows, it supports the Capable AI Coding tier, handling single model workflows well. Current price: approximately $449.
Source: OwnRig methodology
16 GB
288 GB/s
GDDR6
165W
2-slot, 240mm
Builder Capability: Capable AI Coding
Runs 16-22B coding models comfortably, or 32B at reduced quality. Handles single model workflows well.
Inference Backends
The software stacks that matter most for real-world inference on this device.
CUDA
productionPrimary high-performance backend for NVIDIA inference workloads.
Vulkan
stableFallback backend for llama.cpp and related local runtimes.
What it can run
35 models| Arcee Trinity Mini 26B | Q3_K_M | 27 tok/s | Good |
| Arcee Trinity Nano 6B | Q8_0 | 51 tok/s | Excellent |
| Codestral 22B | Q3_K_M | 18 tok/s | Acceptable |
| DeepSeek Coder V2 Lite 16B | Q5_K_M | 50 tok/s | Excellent |
| DeepSeek V3 | Q2_K | – | Not viable |
| FLUX.1 Dev | Q4_K_M | – | Acceptable |
| Gemma 2 27B Instruct | Q4_K_M | 12 tok/s | Acceptable |
| Gemma 3 12B | Q5_K_M | 42 tok/s | Good |
| Gemma 3 27B | Q3_K_M | 6 tok/s | Marginal |
| Gemma 4 26B-A4B | Q3_K_M | 98 tok/s | Excellent |
| Gemma 4 31B | Q3_K_M | 6 tok/s | Marginal |
| Gemma 4 E2B | Q8_0 | 43 tok/s | Good |
| Gemma 4 E4B | Q8_0 | 26 tok/s | Acceptable |
| GigaChat Lightning 10B | Q8_0 | 55 tok/s | Acceptable |
| Llama 3.1 8B Instruct | Q8_0 | 55 tok/s | Excellent |
| Llama 3.2 11B Vision | Q6_K | 38 tok/s | Good |
| Llama 3.2 1B Instruct | Q8_0 | 120 tok/s | Excellent |
| Llama 3.2 3B Instruct | Q8_0 | 75 tok/s | Excellent |
| LLaVA 1.6 13B | Q4_K_M | 22 tok/s | Good |
| NVIDIA Nemotron-3-super-120B-A12B | Q2_K | – | Not viable |
| Phi-3 Medium 14B Instruct | Q5_K_M | 28 tok/s | Good |
| Phi-4 14B | Q4_K_M | 28 tok/s | Good |
| Phi-4 Mini | Q8_0 | 68 tok/s | Excellent |
| Qwen 2.5 14B Instruct | Q4_K_M | 30 tok/s | Good |
| Qwen 2.5 Coder 32B Instruct | Q3_K_M | 10 tok/s | Acceptable |
| Qwen 2.5 Coder 7B Instruct | Q5_K_M | 52 tok/s | Excellent |
| Qwen3-14B Instruct | Q8_0 | 16 tok/s | Acceptable |
| Qwen3.5-122B-A10B | Q3_K_M | – | Not viable |
| Qwen3.5-27B | Q3_K_M | 25 tok/s | Acceptable |
| Qwen3.5-397B (MoE) | Q2_K | – | Not viable |
| Qwen3.6-27B | Q3_K_M | 25 tok/s | Acceptable |
| Stable Diffusion 3 Medium | FP16 | – | Good |
| Stable Diffusion 3.5 Large | FP16 | – | Good |
| StarCoder 2 15B | Q5_K_M | 25 tok/s | Good |
| Whisper Large V3 Turbo | FP16 | – | Excellent |
Showing 35 of 35 entries
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Frequently Asked Questions
- What AI models can NVIDIA GeForce RTX 4060 Ti 16GB run?
- The NVIDIA GeForce RTX 4060 Ti 16GB can run 35 AI models. Top performers include Llama 3.2 1B Instruct, Gemma 4 26B-A4B, Llama 3.2 3B Instruct. See the full compatibility table above for speeds and quality ratings.
- Is NVIDIA GeForce RTX 4060 Ti 16GB good for AI coding?
- Yes. With 16 GB, the NVIDIA GeForce RTX 4060 Ti 16GB handles single-model coding workflows well at the Capable tier.
- How much VRAM does NVIDIA GeForce RTX 4060 Ti 16GB have?
- The NVIDIA GeForce RTX 4060 Ti 16GB has 16 GB of GDDR6 VRAM with 288 GB/s bandwidth.
- Can NVIDIA GeForce RTX 4060 Ti 16GB run 70B models?
- 70B models can run on the NVIDIA GeForce RTX 4060 Ti 16GB with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.
- Is NVIDIA GeForce RTX 4060 Ti 16GB worth it for AI?
- At $449, the NVIDIA GeForce RTX 4060 Ti 16GB offers 16 GB VRAM and runs 35 AI models. It works for smaller models and experimentation.
Own this GPU?
See every AI model it supports, expected performance, and how to build around it.
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