consumer
Rent Nvidia GeForce RTX 4090.
Ada Lovelace
24GB VRAM
450W
2022
From $0.30/hr
Per hour
$0.30
Per day
$7.20
Per week
$50.40
Per month
$216
Provider spread
10 providers ·
up to 72% cheaper at the low end
Cheapest · $0.30/hr on io.net
Median $0.44/hr
Most expensive · $1.06/hr on Fluence
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 $0.30/hr cheapest provider rate
Own on-prem
Electricity per day
—
450W TDP
Workloads
Suitable workloads.
AI models that fit
See all 26 →
Run these on this GPU.
- Gemma 3 27B 1× · int4
- Llama 3.2 11B Vision 1× · fp16
- Qwen 2.5 Coder 32B 1× · fp16
Cloud instances
See all 53 →
Hyperscaler bundles.
Pre-configured on 2 clouds — from $0.33/hr total
($0.33/hr per GPU).
FAQ
Frequently asked.
What AI models can I run on a Nvidia GeForce RTX 4090?
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 24GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia GeForce RTX 4090?
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 4090 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
$0.33/hr
Lowest $/hr per GPU
$0.33/hr
Providers
2
Instance shapes
53
| Provider | Instance | GPUs | vCPU | RAM | Disk | $/hr | $/hr per GPU | |
|---|---|---|---|---|---|---|---|---|
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g.os | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| 4090.16c62g | 1× | 16 | 62 GB | — | $0.33/hr | $0.33/hr | ||
| community-1x | 1× | — | — | — | $0.38/hr | $0.38/hr | ||
| community-1x | 1× | — | — | — | $0.38/hr | $0.38/hr | ||
| community-1x | 1× | — | — | — | $0.38/hr | $0.38/hr | ||
| community-1x | 1× | — | — | — | $0.38/hr | $0.38/hr | ||
| community-1x | 1× | — | — | — | $0.38/hr | $0.38/hr | ||
| community-1x | 1× | — | — | — | $0.38/hr | $0.38/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/hr | ||
| community-1x | 1× | — | — | — | $0.44/hr | $0.44/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 4090?
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 24GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia GeForce RTX 4090?
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 4090 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
INT4
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
INT4
INT4
INT4
INT4
INT4
INT4
NF4
FP8
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 4090.
Gemma 3 27B
by Google DeepMind
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
27B (27B active)
Context
128K tokens
Snug fit, recommended for hobbyist self-hosting
Llama 3.2 11B Vision
by Meta AI
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
11B
Context
128K tokens
Qwen 2.5 Coder 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
33B
Context
128K tokens
24GB just enough — try int8 if OOM
DeepSeek R1 Distill Qwen 14B
by DeepSeek
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
15B
Context
128K tokens
Fits with room for context.
DeepSeek R1 Distill Qwen 7B
by DeepSeek
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
8B
Context
128K tokens
Whisper Large v3
by OpenAI
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
2B
Context
30 tokens
Real-time transcription with headroom.
FLUX.1 Dev
by Black Forest Labs
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
12B
Sweet spot for FLUX.1 Dev.
FLUX.1 Schnell
by Black Forest Labs
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
12B
<2s per image with 4-step distillation.
Stable Diffusion 3.5 Large
by Stability AI
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
8B
Stable Diffusion 3.5 Medium
by Stability AI
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
3B
Llama 3.1 8B
by Meta AI
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
8B
Context
128K tokens
Comfortable single-GPU local target.
Mistral 7B v0.3
by Mistral AI
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
7B
Context
33K tokens
?
INT4
TheDrummer: Skyfall 36B V2
by Thedrummer
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
36B
Context
33K tokens
Stable Diffusion XL
by Stability AI
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
4B
Mistral 7B v0.2
by Mistral AI
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
7B
Context
33K tokens
Qwen3.6 35B A3b Fp8
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
35B
Context
262K tokens
Mixtral 8x22B
by Mistral AI
Required GPUs
4× Nvidia GeForce RTX 4090
Total VRAM
96 GB
Parameters
141B (39B active)
Context
66K tokens
Qwen 2.5 72B
by Alibaba (Qwen Team)
Required GPUs
4× Nvidia GeForce RTX 4090
Total VRAM
96 GB
Parameters
73B
Context
128K tokens
Qwen: Qwen3.5-35B-A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
35B
Context
262K tokens
Llama 3.3 70B
by Meta AI
Required GPUs
4× Nvidia GeForce RTX 4090
Total VRAM
96 GB
Parameters
70B
Context
128K tokens
Quantized only — consumer multi-GPU
DeepSeek R1 Distill Qwen 32B
by DeepSeek
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
33B
Context
128K tokens
FLUX.1 Dev
by Black Forest Labs
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
12B
Halves VRAM, ~95% quality.
Stable Diffusion 3.5 Large
by Stability AI
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
8B
FP8 cuts VRAM by half.
Qwen: Qwen3.6 35B A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
35B
Context
262K tokens
?
INT4
Holo3 35B A3b
by Hcompany
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
35B
Context
262K tokens
LLaVA 34B
by LLaVA Project
Required GPUs
1× Nvidia GeForce RTX 4090
Total VRAM
24 GB
Parameters
34B
Context
4K 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 4090?
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 24GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia GeForce RTX 4090?
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 4090 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.