consumer

Nvidia RTX 4060 Ti mieten.

Name: Nvidia Nvidia RTX 4060 Ti
Brand: Nvidia
SKU: rtx4060ti

Ada Lovelace 16GB VRAM 160W From $0.051/hr

Per hour

$0.051

Per day

$1.22

Per week

$8.57

Per month

$37

Price history

Daily median across providers.

Where to rent it

All providers carrying this GPU.

Provider	$/hr	Range	Offers	30d trend	Updated
Clore.ai	$0.051/hr	$0.024/hr–$0.10/hr	14	↓ 17%	about 24 hours	Launch ↗

Buy vs rent

Should you rent or own?

Your usage

Hours per day Electricity rate ($/kWh)

Rent on cloud

Per day

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Per month

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at $0.051/hr cheapest provider rate

Own on-prem

Electricity per day

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160W TDP

Rent breakeven on $399 MSRP

— days · — months

AI models that fit See all 34 →

Run these on this GPU.

Arize AI Qwen 2 1.5B Instruct 1× · fp16
Qwen 2 (1.5B) 1× · fp16
Qwen 2 Instruct (1.5B) 1× · fp16

FAQ

Frequently asked.

What AI models can I run on a Nvidia RTX 4060 Ti?

The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 16GB of VRAM.

What's the VRAM minimum to run a model on the Nvidia RTX 4060 Ti?

Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.

How does quantization (FP16 vs FP8 vs INT4) affect what fits?

Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.

Can I fine-tune on the Nvidia RTX 4060 Ti or only do inference?

Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.

Where do these GPU-count recommendations come from?

We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.

AI models

Models that run on this GPU.

GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia RTX 4060 Ti.

Nvidia RTX 4060 Ti mieten.

Daily median across providers.

All providers carrying this GPU.

Should you rent or own?

Run these on this GPU.

Frequently asked.

Models that run on this GPU.

Arize AI Qwen 2 1.5B Instruct

Qwen 2 (1.5B)

Qwen 2 Instruct (1.5B)

Qwen2.5 1.5B

Qwen2.5 1.5B Instruct

Qwen3 0.6B

Qwen3 0.6B Base

DeepSeek R1 Distill Qwen 1.5B

Llama 3.2 11B Vision

FLUX.1 Pro

FLUX.1 Dev

FLUX.1 Schnell

Gemma 3 12B

Mistral Nemo 12B

Meta: Llama Guard 4 12B

TheDrummer: UnslopNemo 12B

TheDrummer: Rocinante 12B

nim/nv-mistralai/mistral-nemo-12b-instruct

nim/meta/llama-3.2-11b-vision-instruct

Llama-3.2-11B-Vision-Instruct

GPT-OSS 20B

InternLM 2.5 20B

LiquidAI: LFM2-24B-A2B

Baidu: ERNIE 4.5 21B A3B Thinking

TheDrummer: Cydonia 24B V4.1

Baidu: ERNIE 4.5 21B A3B

Mistral: Mistral Small 3.2 24B

Mistral: Mistral Small 3.1 24B

Mistral: Mistral Small 3

WizardLM-2 8x22B

LFM2-24B-A2B

nim/mistralai/mixtral-8x22b-instruct-v01

Mixtral 8X22b Instruct V0.1

Mistral-Small-3.2-24B-Instruct-2506

AI models on this GPU.