32GB
VRAM
GDDR7 memory
AIVory · GPU Marketplace
Live spot pricingNVIDIA RTX PRO 4500 — compact Blackwell pro with 32 GB of GDDR7.
The mid-tier Blackwell professional card combines 32 GB of GDDR7 with next-generation tensor cores and ECC memory in a power-efficient package. At $0.76/hr on spot, the RTX PRO 4500 offers Blackwell architecture at a fraction of the RTX PRO 6000's price — ideal for teams that need 27B model inference with enterprise driver support but don't require 96 GB of VRAM.
At a glance
RTX PRO 4500 specifications.
Key hardware specs that determine what workloads this GPU handles.
896 GB/s
Memory Bandwidth
peak throughput
200W
TDP
thermal design power
Blackwell
Architecture
NVIDIA GPU architecture
Spot pricing
RTX PRO 4500: 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 PRO 4500.
Tested model-GPU pairings with notes on why each is a good fit.
Qwen 3.5 27B
27B in FP16 needs ~54 GB — runs in INT8 at ~27 GB with 5 GB headroom. Blackwell tensor cores process INT8 matmuls with FP8 internal precision for high-quality output.
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Gemma 4 31B
31B in INT8 at ~16 GB leaves 16 GB for large KV caches. The GDDR7 bandwidth handles high-concurrency serving without becoming the bottleneck.
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Mistral Small 3.2 24B
24B in FP16 uses ~48 GB — tight in FP16 but fits in INT8 at ~24 GB with 8 GB for serving overhead. Blackwell's enhanced quantization minimizes the quality gap.
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Use cases
What the RTX PRO 4500 is built for.
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Blackwell-generation 27B serving at mid-range pricing
The RTX PRO 4500 at $0.76/hr slots between the consumer RTX 5090 ($0.39/hr, no ECC, no pro drivers) and the RTX PRO 6000 ($2.23/hr, 96 GB overkill for 27B). For enterprise teams running Qwen 3.5 27B or similar models that need professional certification without paying for flagship hardware, the PRO 4500 is the Goldilocks option.
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Certified inference for ISV-validated AI applications
NVIDIA's Blackwell professional driver stack carries ISV certifications for enterprise AI applications. The RTX PRO 4500's 32 GB VRAM and Blackwell architecture support the latest NVIDIA AI Enterprise stack, TensorRT-LLM optimizations, and certified configurations that consumer Blackwell cards don't qualify for.
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Energy-efficient edge and branch office AI
At 200W TDP, the RTX PRO 4500 draws 40% less power than the RTX PRO 6000 (350W) while supporting the same Blackwell tensor core operations. Deploy 27B model inference at remote sites, branch offices, or edge data centers where power and cooling budgets are constrained. The card fits standard workstation chassis without requiring server-grade power infrastructure.
FAQ
Common questions.
RTX PRO 4500 vs RTX 5090 — both 32 GB Blackwell, what's different?
The RTX 5090 is consumer Blackwell with 32 GB GDDR7 at 1.79 TB/s bandwidth and $0.39/hr. The RTX PRO 4500 is professional Blackwell with 32 GB GDDR7 at 896 GB/s and $0.76/hr. The RTX 5090 is faster and cheaper for raw inference. The RTX PRO 4500 adds ECC memory, professional drivers, ISV certifications, and lower power consumption (200W vs 575W). Choose the RTX 5090 for speed, the PRO 4500 for enterprise compliance.
RTX PRO 4500 vs RTX 5000 Ada — both 32 GB pro cards?
The RTX 5000 Ada is previous-gen Ada Lovelace at $0.50/hr; the RTX PRO 4500 is current-gen Blackwell at $0.76/hr. Blackwell delivers higher tensor throughput and FP4 support. The RTX 5000 Ada has lower bandwidth (576 vs 896 GB/s) but is 34% cheaper. For cost-sensitive deployments, the RTX 5000 Ada is better value. For maximum performance on the latest architecture, the PRO 4500 justifies the premium.
Is 32 GB enough for production 70B inference?
70B in INT4 needs ~35 GB — exceeds the 32 GB limit. For 70B models, you need the RTX PRO 6000 (96 GB), A100 80GB, or H100. The RTX PRO 4500 targets the 14B-31B model range, where it excels. Attempting to squeeze a 70B model into 32 GB with aggressive quantization degrades output quality to the point where a smaller, unquantized model would outperform it.
Does GDDR7 really compete with HBM for inference?
For memory-bandwidth-bound inference (small batch sizes, long sequences), HBM's higher bandwidth wins. At 896 GB/s, the RTX PRO 4500's GDDR7 is competitive with the A100 40GB's HBM2e (1.6 TB/s) for compute-bound workloads with larger batch sizes. GDDR7's advantage is cost — NVIDIA can offer 32 GB for $0.76/hr because GDDR7 is far cheaper to manufacture than HBM. The tradeoff is lower bandwidth per dollar of VRAM.
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