GeForce RTX 4060 Ti 16GB vs GeForce RTX 4060 Ti 8GB for Local AI

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Which GPU is better for local AI workloads?

Both cards use the AD106 chip with 4352 CUDA cores, 288 GB/s bandwidth, and a 160W TDP, so compute-bound speed is effectively identical. The 8GB version cannot hold 12B\u201314B models even at Q4 without offloading, while the 16GB version runs 14B models at Q8 and Flux-class image models directly.

Spec comparison: what actually differs

The table below is computed live from our hardware database. Positive deltas favor the GeForce RTX 4060 Ti 16GB.

Specification	GeForce RTX 4060 Ti 16GB	GeForce RTX 4060 Ti 8GB	Difference
VRAM	16	8	+100%
Memory bandwidth	288	288	—
Memory type	GDDR6	GDDR6	—
Memory bus	128	128	—
TDP	160	160	—
CUDA cores	4352	4352	—
Architecture	Ada Lovelace	Ada Lovelace	—
Launch MSRP	$499	$399	+25%
Street price	$499	$399	+25%

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

What about price?

Launch MSRP: $499 (16GB) versus $399 (8GB). Check current prices \u2014 both are retiring stock.

Which one should you buy for LLMs and image generation?

NVIDIA · 2023

GeForce RTX 4060 Ti 16GB

Buy the 16GB if you run anything larger than 8B models or want Flux usable \u2014 the $100 is the best money you can spend on this GPU.

Full specs & benchmarks →

NVIDIA · 2023

GeForce RTX 4060 Ti 8GB

Buy the 8GB only if your workloads are 7B\u20138B Q4 LLMs, SD 1.5, or Whisper, and the price gap is wide.

Full specs & benchmarks →

Is it faster for LLM inference?

For local LLMs the 16GB is the only sensible choice: a 14B model at Q8 needs about 15GB, which the 8GB card cannot load. On 7B\u20138B models both cards perform the same.

How does it handle image generation?

SDXL fits on 8GB but tightly; Flux-class models require aggressive offloading on the 8GB card. The 16GB version runs Flux and SDXL without those compromises.

Which AI models fit on each card?

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

Is the RTX 4060 Ti 8GB or 16GB better for LLMs?

The 16GB, decisively. Both cards have the same 4352 CUDA cores and 288 GB/s bandwidth, but a 14B model at Q8 needs about 15GB of VRAM, which only the 16GB version holds.

Does more VRAM make the 4060 Ti 16GB faster?

No. Memory bandwidth stays at 288 GB/s on both, so models that fit in 8GB generate tokens at the same speed. VRAM decides which models load, not how fast they run.

Can the 8GB version run Flux?

Only with CPU offloading, which drops generation to a fraction of GPU-only speed. Flux needs roughly 12GB or more for comfortable use; the 16GB handles it directly.