consumer
Alquilar Nvidia Nvidia GeForce RTX 5090.
Blackwell
32GB VRAM
From $0.18/hr
Per hour
$0.18
Per day
$4.34
Per week
$30.41
Per month
$130
Provider spread
5 providers ·
up to 73% cheaper at the low end
Cheapest · $0.18/hr on Vast.ai
Median $0.34/hr
Most expensive · $0.68/hr on io.net
Price history
Daily median across providers.
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Where to rent it
All providers carrying this GPU.
Workloads
Suitable workloads.
FAQ
Frequently asked.
What AI models can I run on a Nvidia GeForce RTX 5090?
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 32GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia GeForce RTX 5090?
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 5090 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
INT4
FP16
FP16
FP16
FP16
FP16
FP16
FP16
INT4
INT4
INT4
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 Nvidia GeForce RTX 5090.
nim/nvidia/llama-3.3-nemotron-super-49b-v1
by Nvidia
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
49B
Context
16K tokens
Gemma 3 27B
by Google DeepMind
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
27B (27B active)
Context
128K tokens
Llama 3.2 11B Vision
by Meta AI
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
11B
Context
128K tokens
Qwen 2.5 Coder 32B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
33B
Context
128K tokens
DeepSeek R1 Distill Qwen 14B
by DeepSeek
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
15B
Context
128K tokens
FLUX.1 Dev
by Black Forest Labs
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
12B
FLUX.1 Schnell
by Black Forest Labs
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
12B
Stable Diffusion 3.5 Large
by Stability AI
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
8B
Llama 3.1 8B
by Meta AI
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
8B
Context
128K tokens
NVIDIA: Llama 3.3 Nemotron Super 49B V1.5
by Nvidia
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
49B
Context
131K tokens
DeepSeek R1 Distill Qwen 32B
by DeepSeek
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
33B
Context
128K tokens
Best home-lab option.
?
INT4
TheDrummer: Skyfall 36B V2
by Thedrummer
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
36B
Context
33K tokens
DeepSeek R1 Distill Llama 70B
by DeepSeek
Required GPUs
2× Nvidia GeForce RTX 5090
Total VRAM
64 GB
Parameters
70B
Context
128K tokens
Llama 3.1 70B
by Meta AI
Required GPUs
2× Nvidia GeForce RTX 5090
Total VRAM
64 GB
Parameters
70B
Context
128K tokens
Qwen3.6 35B A3b Fp8
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
35B
Context
262K tokens
Llama 3.3 70B
by Meta AI
Required GPUs
2× Nvidia GeForce RTX 5090
Total VRAM
64 GB
Parameters
70B
Context
128K tokens
Qwen: Qwen3.5-35B-A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
35B
Context
262K tokens
Qwen: Qwen3.6 35B A3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GeForce RTX 5090
Total VRAM
32 GB
Parameters
35B
Context
262K tokens
Renting for inference?
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FAQ
AI models on this GPU.
What AI models can I run on a Nvidia GeForce RTX 5090?
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 32GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia GeForce RTX 5090?
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 5090 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.