workstation
Thuê Nvidia RTX 5000 Ada.
Ada Lovelace
32GB VRAM
250W
From $0.44/hr
Per hour
$0.44
Per day
$10.56
Per week
$73.92
Per month
$317
Provider spread
2 providers ·
up to 3% cheaper at the low end
Cheapest · $0.44/hr on TensorDock
Median $0.45/hr
Most expensive · $0.46/hr on RunPod
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 $0.44/hr cheapest provider rate
Own on-prem
Electricity per day
—
250W TDP
Rent breakeven on $4,000 MSRP
— days
· — months
Workloads
Suitable workloads.
FAQ
Frequently asked.
What AI models can I run on a Nvidia RTX 5000 Ada?
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 RTX 5000 Ada?
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 5000 Ada 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
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
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 RTX 5000 Ada.
Llama 3.2 11B Vision
by Meta AI
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
11B
Context
128K tokens
FLUX.1 Pro
by Black Forest Labs
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
12B
FLUX.1 Dev
by Black Forest Labs
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
12B
FLUX.1 Schnell
by Black Forest Labs
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
12B
Gemma 3 12B
by Google DeepMind
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
12B
Context
128K tokens
Mistral Nemo 12B
by Mistral AI
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
12B
Context
128K tokens
Meta: Llama Guard 4 12B
by Meta AI
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
12B
Context
164K tokens
TheDrummer: UnslopNemo 12B
by Thedrummer
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
12B
Context
33K tokens
TheDrummer: Rocinante 12B
by Thedrummer
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
12B
Context
33K tokens
nim/nv-mistralai/mistral-nemo-12b-instruct
by Nvidia
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
12B
Context
16K tokens
nim/meta/llama-3.2-11b-vision-instruct
by Nvidia
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
11B
Context
16K tokens
Llama-3.2-11B-Vision-Instruct
by Meta AI
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
11B
Context
131K tokens
GPT-OSS 20B
by OpenAI
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
20B (4B active)
Context
128K tokens
InternLM 2.5 20B
by Shanghai AI Lab
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
20B
Context
1M tokens
LiquidAI: LFM2-24B-A2B
by Liquid
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
24B
Context
128K tokens
Baidu: ERNIE 4.5 21B A3B Thinking
by Baidu
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
21B
Context
131K tokens
TheDrummer: Cydonia 24B V4.1
by Thedrummer
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
24B
Context
131K tokens
Baidu: ERNIE 4.5 21B A3B
by Baidu
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
21B
Context
131K tokens
Mistral: Mistral Small 3.2 24B
by Mistral AI
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
24B
Context
128K tokens
Mistral: Mistral Small 3.1 24B
by Mistral AI
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
24B
Context
128K tokens
Mistral: Mistral Small 3
by Mistral AI
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
24B
Context
33K tokens
WizardLM-2 8x22B
by Microsoft
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
22B
Context
66K tokens
LFM2-24B-A2B
by Togethercomputer
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
24B
Context
33K tokens
nim/mistralai/mixtral-8x22b-instruct-v01
by Mistral AI
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
22B
Context
16K tokens
Mixtral 8X22b Instruct V0.1
by Mistral AI
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
22B
Context
66K tokens
Mistral-Small-3.2-24B-Instruct-2506
by Mistral AI
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
24B
Context
128K tokens
NVIDIA: Llama 3.3 Nemotron Super 49B V1.5
by Nvidia
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
49B
Context
131K tokens
nim/nvidia/llama-3.3-nemotron-super-49b-v1
by Nvidia
Required GPUs
1× Nvidia RTX 5000 Ada
Total VRAM
32 GB
Parameters
49B
Context
16K tokens
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FAQ
AI models on this GPU.
What AI models can I run on a Nvidia RTX 5000 Ada?
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 RTX 5000 Ada?
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 5000 Ada 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.