consumer
Thuê Nvidia GeForce RTX 4060.
Ada Lovelace
8GB VRAM
115W
2023
From $0.051/hr
Per hour
$0.051
Per day
$1.22
Per week
$8.55
Per month
$37
Provider spread
3 providers ·
up to 61% cheaper at the low end
Cheapest · $0.051/hr on Nosana
Median $0.083/hr
Most expensive · $0.13/hr on Theta EdgeCloud
Price history
Daily median across providers.
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Where to rent it
All providers carrying this GPU.
Buy vs rent
Should you rent or own?
Your usage
Rent on cloud
Per day
—
Per month
—
at $0.051/hr cheapest provider rate
Own on-prem
Electricity per day
—
115W TDP
AI models that fit
See all 30 →
Run these on this GPU.
- Stable Diffusion 1.5 1× · fp16
- Llama 3.2 3B 1× · fp16
- Mistral Nemo 12B 1× · int4
Cloud instances
See all 19 →
Hyperscaler bundles.
Pre-configured on 1 clouds — from $0.13/hr total
($0.13/hr per GPU).
FAQ
Frequently asked.
What AI models can I run on a Nvidia GeForce RTX 4060?
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 8GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia GeForce RTX 4060?
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 GeForce RTX 4060 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.
Cloud instance options
Pre-configured instances on hyperscalers.
Whole-instance bundles (GPU + vCPU + RAM + disk) on the major clouds. Per-GPU rate often drops as the count rises. View = spec page · Launch = sign up (affiliate).
Cheapest bundle
$0.13/hr
Lowest $/hr per GPU
$0.13/hr
Providers
1
Instance shapes
19
| Provider | Instance | GPUs | vCPU | RAM | Disk | $/hr | $/hr per GPU | |
|---|---|---|---|---|---|---|---|---|
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr | ||
| community-1x | 1× | — | — | — | $0.13/hr | $0.13/hr |
Looking for the cheapest rate?
Hyperscaler bundles include managed networking + SLAs. Raw per-GPU rental on P2P marketplaces is typically 3–10× cheaper.
See raw rental rates on the Overview tab →
FAQ
Cloud instances — common questions.
What AI models can I run on a Nvidia GeForce RTX 4060?
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 8GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia GeForce RTX 4060?
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 GeForce RTX 4060 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
FP16
FP16
INT4
INT4
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
INT4
INT4
INT4
INT4
FP8
FP8
FP8
FP8
FP8
FP8
FP8
INT4
INT4
INT4
INT4
INT4
Models that run on this GPU.
GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia GeForce RTX 4060.
Stable Diffusion 1.5
by Stability AI
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
1B
Runs on practically anything from 2020 onward.
Llama 3.2 3B
by Meta AI
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
3B
Context
128K tokens
Mistral Nemo 12B
by Mistral AI
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
12B
Context
128K tokens
Gemma 3 12B
by Google DeepMind
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
12B
Context
128K tokens
Stable Diffusion 3.5 Medium
by Stability AI
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
3B
Qwen 2.5 3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
3B
Context
33K tokens
Gemma 2 2B
by Google DeepMind
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
3B
Context
8K tokens
Mistral: Ministral 3 3B 2512
by Mistral AI
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
3B
Context
131K tokens
IBM: Granite 4.0 Micro
by IBM Research
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
3B
Context
131K tokens
Qwen2.5 3B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
3B
Context
33K tokens
Meta Llama 3.2 3B Instruct
by Meta AI
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
3B
Context
131K tokens
granite-4.2-3b
by IBM Research
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
3B
Context
131K tokens
FLUX.1 Dev
by Black Forest Labs
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
12B
FLUX.1 Schnell
by Black Forest Labs
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
12B
FLUX.1 Pro
by Black Forest Labs
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
12B
Meta: Llama Guard 4 12B
by Meta AI
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
12B
Context
164K tokens
Arize AI Qwen 2 1.5B Instruct
by Togethercomputer
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
5B
Context
33K tokens
Qwen 2 (1.5B)
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
5B
Context
33K tokens
Qwen 2 Instruct (1.5B)
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
5B
Context
33K tokens
Qwen2.5 1.5B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
5B
Context
131K tokens
Qwen2.5 1.5B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
5B
Context
33K tokens
Qwen3 0.6B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
6B
Context
41K tokens
Qwen3 0.6B Base
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
6B
Context
33K tokens
Llama 3.2 11B Vision
by Meta AI
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
11B
Context
128K tokens
?
INT4
TheDrummer: UnslopNemo 12B
by Thedrummer
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
12B
Context
33K tokens
?
INT4
TheDrummer: Rocinante 12B
by Thedrummer
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
12B
Context
33K tokens
nim/nv-mistralai/mistral-nemo-12b-instruct
by Nvidia
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
12B
Context
16K tokens
nim/meta/llama-3.2-11b-vision-instruct
by Nvidia
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
11B
Context
16K tokens
Llama-3.2-11B-Vision-Instruct
by Meta AI
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
11B
Context
131K tokens
Gemma 4 12B It
by Google DeepMind
Required GPUs
1× Nvidia GeForce RTX 4060
Total VRAM
8 GB
Parameters
12B
Context
262K tokens
Renting for inference?
Pair these models with the cheapest provider on the rental table.
See rental rates on the Overview tab →
FAQ
AI models on this GPU.
What AI models can I run on a Nvidia GeForce RTX 4060?
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 8GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia GeForce RTX 4060?
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 GeForce RTX 4060 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.