How large a large language model can fit on NVIDIA RTX 4000 Ada VRAM?

💡 Answer

NVIDIA RTX 4000 Ada spec breakdown — 20 GB GDDR6 @ 360 GB/s, 107/26.7 TFLOPS FP16/FP32, Ada Lovelace generation, 130W, 2023.

For production ML planning: if your model fits in 20 GB at your target precision, NVIDIA RTX 4000 Ada is a candidate. If your workload is latency-sensitive real-time serving, bandwidth and clock speed matter more than raw FLOPS. If you're pre-training from scratch on large batches, the FP16 number predicts wall-clock throughput best.

Rent NVIDIA RTX 4000 Ada on DigitalOcean from $0.76/hr — check live availability and deploy.

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DigitalOcean GPU Provider Review & Key Facts (April 2026)

Snapshot of DigitalOcean: GPU models, pricing, billing granularity, infrastructure, developer tools, support channels, and compliance. Data verified April 2026.

DigitalOcean GPU Provider Review & Key Facts (April 2026)
DigitalOcean
Simple, scalable GPU cloud for AI/ML
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Overview
Trustpilot Rating 4.6
Headquarters United States
Provider Type N/A
Best For AI training inference fine-tuning LLM deployment LLM serving computer vision startups generative AI research
GPU Hardware
GPU Models RTX 4000 Ada RTX 6000 Ada L40S MI300X H100 SXM H200
Max VRAM (GB) 192
Max GPUs/Instance 8
Interconnect NVLink
Pricing
Starting Price ($/hr) $0.76/hr
Billing Granularity Per-second
Spot/Preemptible No
Reserved Discounts N/A
Free Credits $200 free credit for 60 days
Egress Fees None (included in plan)
Storage 500-720 GiB NVMe boot (included), 5 TiB NVMe scratch on larger configs, Volumes at $0.10/GiB/mo
Infrastructure
Regions New York (NYC2), Toronto (TOR1), Atlanta (ATL1), Richmond (RIC1), Amsterdam (AMS3)
Uptime SLA 99%
Developer Experience
Frameworks PyTorch TensorFlow Jupyter Miniconda CUDA ROCm Hugging Face
Docker Support Yes
SSH Access Yes
Jupyter Notebooks Yes
API / CLI Yes
Setup Time Minutes
Kubernetes Support Yes
Business Terms
Min Commitment None
Compliance SOC 2 Type II SOC 3 HIPAA (with BAA) CSA STAR Level 1
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