32GB
VRAM
GDDR6 memory
AIVory · GPU Marketplace
Live spot pricingNVIDIA RTX 5000 Ada — 32 GB professional reliability at workstation scale.
The mid-range professional Ada card brings 32 GB of ECC-protected GDDR6 with certified drivers and enterprise support. At $0.50/hr on spot, the RTX 5000 Ada slots between consumer cards and data center GPUs — offering workstation-grade stability for teams that need 27B model serving with driver certification and error-corrected memory.
At a glance
RTX 5000 Ada specifications.
Key hardware specs that determine what workloads this GPU handles.
576 GB/s
Memory Bandwidth
peak throughput
250W
TDP
thermal design power
Ada Lovelace
Architecture
NVIDIA GPU architecture
Spot pricing
RTX 5000 Ada: live hourly rates.
Every provider offering this GPU on the spot market, sorted cheapest first.
Prices in USD per GPU-hour · spot instances · sorted cheapest first
Recommended models
AI models that run well on RTX 5000 Ada.
Tested model-GPU pairings with notes on why each is a good fit.
Gemma 4 31B
31B parameters in INT8 need ~16 GB, leaving 16 GB for large KV caches and high concurrency. ECC memory prevents corruption during long inference sessions.
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Command R 35B
35B parameters in INT8 fit on the RTX 5000 Ada with room for RAG context windows. The professional drivers ensure stable multi-day uptime without the driver crashes that plague consumer cards.
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Qwen 3.5 27B
Runs in full FP16 on the RTX 5000 Ada with 5 GB headroom. The 576 GB/s bandwidth sustains token generation for interactive applications.
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Use cases
What the RTX 5000 Ada is built for.
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Serving 27B-35B models with enterprise driver support
Teams in regulated environments need GPU drivers that are ISV-certified and carry long-term support. Consumer cards like the RTX 5090 offer the same 32 GB VRAM but with GeForce drivers that update frequently and lack enterprise validation. The RTX 5000 Ada provides the same Ada Lovelace performance with professional driver stability at $0.50/hr.
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Workstation-based AI development and serving
Developers who run inference locally on workstations benefit from the RTX 5000 Ada's display output and professional features. Run a 27B model in the background while using the same GPU for IDE rendering, debugging visualizations, and model experimentation. The 250W TDP keeps the card quiet in workstation chassis.
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Multi-GPU workstation configurations for 70B models
Two RTX 5000 Ada cards (64 GB total) at $1.00/hr handle Llama 3.3 70B in INT4 with PCIe-based tensor parallelism. While not as fast as a single A100 80GB ($1.14/hr), the dual RTX 5000 Ada setup is cheaper and provides redundancy — if one card fails, you can still serve smaller models on the remaining card.
FAQ
Common questions.
RTX 5000 Ada vs RTX 5090 — both have 32 GB, what's different?
The RTX 5090 is a consumer Blackwell card with 32 GB GDDR7 at 1.79 TB/s bandwidth — significantly faster raw memory throughput. The RTX 5000 Ada is a professional Ada card with 32 GB GDDR6 ECC at 576 GB/s. Choose the RTX 5090 for maximum inference speed ($0.39/hr), the RTX 5000 Ada for ECC protection, ISV-certified drivers, and enterprise support ($0.50/hr).
Is the RTX 5000 Ada worth the price over the consumer RTX 4090?
The RTX 4090 has 24 GB at $0.29/hr; the RTX 5000 Ada has 32 GB at $0.50/hr. If your model fits in 24 GB, the RTX 4090 is faster (1.01 TB/s bandwidth) and cheaper. If you need the extra 8 GB for 27B+ models in FP16, the RTX 5000 Ada is the cheaper option compared to jumping to a 48 GB card.
Does the RTX 5000 Ada support NVLink?
Yes. The RTX 5000 Ada supports NVLink for two-card configurations, providing up to 112 GB/s bidirectional bandwidth between cards. This is significantly faster than PCIe for tensor-parallel inference and enables two RTX 5000 Ada cards to act as a unified 64 GB GPU for larger models.
How does ECC affect inference performance?
ECC memory adds ~2-3% latency overhead for error checking and correction. On the RTX 5000 Ada, this means token generation is marginally slower than a non-ECC card with the same specs. In practice, the difference is unnoticeable for interactive inference and irrelevant for batch workloads. The tradeoff is guaranteed output correctness — ECC prevents the rare but real bit-flip errors that can produce garbled model outputs.
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