GeForce RTX 4060 vs GeForce RTX 4060 Ti 16GB for Local AI

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Which GPU is better for local AI workloads?

The 4060 Ti 16GB's 16GB holds 14B at Q8 and batches SDXL; its 288 GB/s is barely above the 4060's 272 GB/s, so speed gains are small — capacity is the whole story. The 4060 at $299/115W is the efficient 8GB option.

Spec comparison: what actually differs

The table below is computed live from our hardware database. Positive deltas favor the GeForce RTX 4060.

Specification	GeForce RTX 4060	GeForce RTX 4060 Ti 16GB	Difference
VRAM	8	16	-50%
Memory bandwidth	272	288	-6%
Memory type	GDDR6	GDDR6	—
Memory bus	128	128	—
TDP	115	160	-28%
CUDA cores	3072	4352	-29%
Architecture	Ada Lovelace	Ada Lovelace	—
Launch MSRP	$299	$499	-40%
Street price	$299	$499	-40%

Specs from our sourced product database. See how we source data.

What about price?

Launch MSRP: $299 (4060) versus $499 (4060 Ti 16GB).

Which one should you buy for LLMs and image generation?

NVIDIA · 2023

GeForce RTX 4060

Buy the 4060 for efficiency and 8B-class budgets.

Full specs & benchmarks →

NVIDIA · 2023

GeForce RTX 4060 Ti 16GB

Buy the 4060 Ti 16GB used for capacity — or the 5060 Ti 16GB new for the same money with 448 GB/s.

Full specs & benchmarks →

Is it faster for LLM inference?

14B Q8 needs the Ti; 8B decode is nearly equal between them.

How does it handle image generation?

Ti's 16GB gives real batching headroom over 8GB.

Which AI models fit on each card?

Computed from our model VRAM database at Q4 quantization: GeForce RTX 4060 has 8 GB, GeForce RTX 4060 Ti 16GB has 16 GB.

Fit only on the GeForce RTX 4060 Ti 16GB (16 GB)

Qwen3.8-27B ✓ Qwen3.5-27B ✓ Qwen3.6-27B ✓ Gemma 3 27B ✓ Wan 2.2 (A14B & TI2V-5B) ✓ OUI 1 ✓ Gemma 4 26B A4B IT ✓ Mistral Small 3.2 24B ✓ Devstral Small 2 24B Instruct ✓ Magistral-Small-2509 ✓ Gpt Oss 20B ✓ Maple Preview ✓ Ling Mini 2.0 ✓ Deepseek R1 Distill Qwen 14B ✓ Qwen3 14B ✓ Confucius4-T3PO ✓ WebWorld-14B ✓ ContextPilot 14B ✓ Phi 4 ✓ Phi 3 Medium ✓ Ministral 3 14B Instruct ✓ Ministral 3 14B Reasoning ✓

Fit on both cards

Gemma 3 12B Mellum 2.1 Thinking FLUX.1 dev Gemma 4 12B IT Ornith 1.5 9B OxCoder 9B Gemma 2 9B K2 Horizon 7B NeoHorse 1 9B Ministral 3 8B Reasoning Ministral 3 8B Instruct Granite 4.2 8B LFM2.5 8B A1B Qwen 3 8B ALoDLM 8B Deepseek R1 Distill Llama 8B Llama 3.1 8B Stable Diffusion 3.5 Large Gemma 4 E4B IT Edge0 8B A1B (preview) Ling-3.0-tiny Qwen2 5 7B ZGCM-1 7B Mistral 7B V03 Gemma 4 E2B IT K2 Horizon 3.7B Gemma 3 4B Ministral 3 3B Reasoning NeoHorse 1 4B Nanbeige4.2 3B Spark X2.5 4B Qwen3 4B Ministral 3 3B Instruct Granite 4.2 3B Stable Diffusion XL 1.0 Llama 3 2 3B Granite Swash 3B A600M LFM2.5 2.6B MiniCPM5 2B Qwen3 1 7B Spark X2.5 1.7B Whisper large-v3 Llama 3 2 1B K2 Horizon 0.9B Coqui XTTS-v2 Qwen3 0 6B GPT-X3 150M

Q4_K_M-equivalent sizes; context and quantization choices shift real limits. Check each model page for full quantization tables.

Frequently asked questions

4060 vs 4060 Ti 16GB — is the price jump worth it for AI?

If you need 12B–14B models, yes — 16GB is required. If 8B models suffice, the 4060 saves $200 with nearly the same decode speed.