NVIDIA

NVIDIA GeForce RTX 4060 Ti 16GB

Name: NVIDIA GeForce RTX 4060 Ti 16GB
Brand: NVIDIA
Price: 449 USD
Availability: InStock

16 GB · 288 GB/s

$449

Updated 2026-03-01

The NVIDIA GeForce RTX 4060 Ti 16GB with 16 GB GDDR6 VRAM can handle 21 AI models across chat, coding, ai_coding. Best performance: Llama 3.2 1B Instruct at 120 tok/s (excellent). For AI coding workflows, it supports the Capable AI Coding tier — handles single model workflows well. Current price: approximately $449.

— OwnRig methodology, data updated 2026-03-01

Builder Capability: Capable AI Coding

Runs 16-22B coding models comfortably, or 32B at reduced quality. Handles single model workflows well.

Specifications

Memory

Capacity: 16 GB
Bandwidth: 288 GB/s
Type: GDDR6

Power & Form Factor

TDP: 165W
Rec. PSU: 550W
Slots: 2-slot
Length: 240mm

Compatible Models

Model	Quant	Speed	Rating	Notes
Llama 3.1 8B Instruct	Q8_0	55 tok/s	Excellent	Full quality Q8 fits with 7GB headroom. Excellent everyday performance.
Qwen 2.5 Coder 32B Instruct	Q3_K_M	10 tok/s	Acceptable	Tight fit at Q3 (14.8GB on 16GB). Usable for code completion but Q3 quality loss is noticeable. Low bandwidth (288 GB/s) limits speed.
DeepSeek Coder V2 Lite 16B	Q5_K_M	50 tok/s	Excellent	Excellent fit — Q5 quality at 10.9GB on 16GB. Fast inference thanks to MoE sparsity.
FLUX.1 Dev	Q4_K_M	—	Acceptable	Q4 FLUX at ~7.2GB fits on 16GB. Slower than SDXL (~30-45 seconds per image) but significantly better quality.
Gemma 2 27B Instruct	Q4_K_M	12 tok/s	Acceptable	Tight fit at 15.5GB on 16GB. Works but slow due to bandwidth constraints.
Codestral 22B	Q3_K_M	18 tok/s	Acceptable	Q3 at 10.3GB on 16GB. Quality compromise but functional for code completion.
Phi-3 Medium 14B Instruct	Q5_K_M	28 tok/s	Good	Q5 at 9.7GB fits well on 16GB. Good reasoning model at a size that's practical on mid-range hardware.
StarCoder 2 15B	Q5_K_M	25 tok/s	Good	Q5 at 10.7GB on 16GB. Good FIM support makes it suitable for code completion backends.
LLaVA 1.6 13B	Q4_K_M	22 tok/s	Good	Q4 at 7.7GB fits on 16GB. First token is slower due to vision encoder processing.
Gemma 3 12B	Q5_K_M	42 tok/s	Good	Q5 fits with headroom. Strong quality on 16GB.
Qwen 2.5 Coder 7B Instruct	Q5_K_M	52 tok/s	Excellent	Excellent coding performance on 16GB. Q5 quality with headroom.
Phi-4 14B	Q4_K_M	28 tok/s	Good	14B at Q4 fits on 16GB. Good reasoning model for mid-range.
Qwen 2.5 14B Instruct	Q4_K_M	30 tok/s	Good	14B at Q4 fits on 16GB. Good general-purpose performance.
Stable Diffusion 3 Medium	FP16	—	Acceptable	SD3 Medium ~12GB VRAM. Fits on 16GB. Slower than SDXL but better quality.
Llama 3.2 3B Instruct	Q8_0	75 tok/s	Excellent	Q8_0 fits with 12GB headroom. 288 GB/s bandwidth delivers excellent 3B inference.
Llama 3.2 1B Instruct	Q8_0	120 tok/s	Excellent	1B class runs at maximum speed. Minimal VRAM footprint.
Phi-4 Mini	Q8_0	68 tok/s	Excellent	Q8_0 fits with 11GB headroom. Good speed for 3.8B reasoning model.
Whisper Large V3 Turbo	FP16	—	Excellent	Real-time transcription. 0.81B params, 1.6GB VRAM. Fits with 14GB headroom.
Stable Diffusion 3.5 Large	FP16	—	Good	FP16 at 12.5GB fits on 16GB with headroom. ~12s per image.
Gemma 3 27B	Q3_K_M	6 tok/s	Marginal	Barely fits at Q3_K_M (13.3GB). Low bandwidth (288 GB/s) severely limits throughput. Usable but slow.
DeepSeek V3	Q2_K	—	Not Viable	671B MoE model requires 115GB+ at Q2_K. 16GB insufficient. Would need 128GB+ unified memory.

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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 21 AI models. Top performers include Llama 3.2 1B Instruct, Llama 3.2 3B Instruct, Phi-4 Mini. 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 21 AI models. It's a good entry point for smaller models and experimentation.

Generation: Ada Lovelace. Last updated: 2026-03-01.

Compatible Models

Model	Quant	Speed	Rating	Notes
Llama 3.1 8B Instruct	Q8_0	55 tok/s	Excellent	Full quality Q8 fits with 7GB headroom. Excellent everyday performance.
Qwen 2.5 Coder 32B Instruct	Q3_K_M	10 tok/s	Acceptable	Tight fit at Q3 (14.8GB on 16GB). Usable for code completion but Q3 quality loss is noticeable. Low bandwidth (288 GB/s) limits speed.
DeepSeek Coder V2 Lite 16B	Q5_K_M	50 tok/s	Excellent	Excellent fit — Q5 quality at 10.9GB on 16GB. Fast inference thanks to MoE sparsity.
FLUX.1 Dev	Q4_K_M	—	Acceptable	Q4 FLUX at ~7.2GB fits on 16GB. Slower than SDXL (~30-45 seconds per image) but significantly better quality.
Gemma 2 27B Instruct	Q4_K_M	12 tok/s	Acceptable	Tight fit at 15.5GB on 16GB. Works but slow due to bandwidth constraints.
Codestral 22B	Q3_K_M	18 tok/s	Acceptable	Q3 at 10.3GB on 16GB. Quality compromise but functional for code completion.
Phi-3 Medium 14B Instruct	Q5_K_M	28 tok/s	Good	Q5 at 9.7GB fits well on 16GB. Good reasoning model at a size that's practical on mid-range hardware.
StarCoder 2 15B	Q5_K_M	25 tok/s	Good	Q5 at 10.7GB on 16GB. Good FIM support makes it suitable for code completion backends.
LLaVA 1.6 13B	Q4_K_M	22 tok/s	Good	Q4 at 7.7GB fits on 16GB. First token is slower due to vision encoder processing.
Gemma 3 12B	Q5_K_M	42 tok/s	Good	Q5 fits with headroom. Strong quality on 16GB.
Qwen 2.5 Coder 7B Instruct	Q5_K_M	52 tok/s	Excellent	Excellent coding performance on 16GB. Q5 quality with headroom.
Phi-4 14B	Q4_K_M	28 tok/s	Good	14B at Q4 fits on 16GB. Good reasoning model for mid-range.
Qwen 2.5 14B Instruct	Q4_K_M	30 tok/s	Good	14B at Q4 fits on 16GB. Good general-purpose performance.
Stable Diffusion 3 Medium	FP16	—	Acceptable	SD3 Medium ~12GB VRAM. Fits on 16GB. Slower than SDXL but better quality.
Llama 3.2 3B Instruct	Q8_0	75 tok/s	Excellent	Q8_0 fits with 12GB headroom. 288 GB/s bandwidth delivers excellent 3B inference.
Llama 3.2 1B Instruct	Q8_0	120 tok/s	Excellent	1B class runs at maximum speed. Minimal VRAM footprint.
Phi-4 Mini	Q8_0	68 tok/s	Excellent	Q8_0 fits with 11GB headroom. Good speed for 3.8B reasoning model.
Whisper Large V3 Turbo	FP16	—	Excellent	Real-time transcription. 0.81B params, 1.6GB VRAM. Fits with 14GB headroom.
Stable Diffusion 3.5 Large	FP16	—	Good	FP16 at 12.5GB fits on 16GB with headroom. ~12s per image.
Gemma 3 27B	Q3_K_M	6 tok/s	Marginal	Barely fits at Q3_K_M (13.3GB). Low bandwidth (288 GB/s) severely limits throughput. Usable but slow.
DeepSeek V3	Q2_K	—	Not Viable	671B MoE model requires 115GB+ at Q2_K. 16GB insufficient. Would need 128GB+ unified memory.

Frequently Asked Questions

What AI models can NVIDIA GeForce RTX 4060 Ti 16GB run?

The NVIDIA GeForce RTX 4060 Ti 16GB can run 21 AI models. Top performers include Llama 3.2 1B Instruct, Llama 3.2 3B Instruct, Phi-4 Mini. 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 21 AI models. It's a good entry point for smaller models and experimentation.