32GB
VRAM
GDDR7 memory
AIVory · GPU Marketplace
Live spot pricingNVIDIA RTX 5090 — Blackwell power, consumer price.
32 GB of GDDR7 on NVIDIA's newest Blackwell architecture. The first consumer GPU where 27B models fit in full FP16 — no quantization, no quality loss. Improved power efficiency per TFLOP and growing spot availability as supply ramps through 2026.
At a glance
RTX 5090 specifications.
Key hardware specs that determine what workloads this GPU handles.
1.79 TB/s
Memory Bandwidth
peak throughput
575W
TDP
thermal design power
Blackwell
Architecture
NVIDIA GPU architecture
Spot pricing
RTX 5090: 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 5090.
Tested model-GPU pairings with notes on why each is a good fit.
Qwen 3.5 27B
Fits in full FP16 on the 5090's 32 GB — the defining use case that separates this GPU from the 24 GB RTX 4090.
View model pricing →
Mistral Small 3.2 24B
24B parameters in FP16 need ~48 GB... but in INT8 it fits in 24 GB. On the 5090, FP16 fits comfortably with room for KV cache.
View model pricing →
Gemma 4 31B (4-bit)
4-bit quantization brings 31B parameters to ~18 GB. Run Gemma 4 with massive batch sizes on a single consumer card.
View model pricing →
Mistral Small 24B
In FP16, fits with 8 GB of headroom for KV cache. The 5090's 1.79 TB/s bandwidth keeps attention fast at long contexts.
View model pricing →
Use cases
What the RTX 5090 is built for.
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Running 24-27B models without quantization
This is what the extra 8 GB over the RTX 4090 buys you. Models like Qwen 3.5 27B and Mistral Small 24B fit in full FP16 on the RTX 5090 with room for KV cache. On a 24 GB card you must quantize to INT8, losing some quality. The 5090 eliminates that trade-off at a consumer price point — full precision inference for the growing class of 24-27B models that represent the best quality-per-parameter in mid-2026.
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Next-gen power efficiency at scale
Blackwell architecture delivers more TFLOPS per watt than Ada Lovelace. While the 5090's 575W TDP is higher than the 4090's 450W in absolute terms, the performance-per-watt improvement means you process more tokens for each kilowatt-hour consumed. At scale with many GPUs, this translates to meaningful electricity savings over the lifetime of a deployment.
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Early adopter advantage on spot
RTX 5090 spot availability is growing as supply ramps through 2026. Early spot prices start around $0.39/hr on Vast.ai, with RunPod at $1.17/hr. As more units enter the market from gamers and creators upgrading from 4090s, expect prices to fall further. Locking in 5090 spot capacity now positions you ahead of the demand curve.
FAQ
Common questions.
RTX 5090 vs RTX 4090 — is the upgrade worth the higher hourly rate?
The decision comes down to VRAM. If every model you run fits in 24 GB, the 4090 at $0.29/hr beats the 5090 at $0.39/hr on pure cost. But if you run 24-27B models that require INT8 quantization on the 4090, the 5090's 32 GB lets you serve them in FP16 with measurably better output quality. The 5090 also has 77% higher memory bandwidth (1.79 vs 1.01 TB/s), which improves throughput on memory-bound workloads.
What does GDDR7 mean in practice vs GDDR6X?
GDDR7 delivers nearly double the per-pin bandwidth of GDDR6X. For the RTX 5090, this translates to 1.79 TB/s memory bandwidth versus the 4090's 1.01 TB/s — a 77% increase. In practice, this means faster KV cache reads during inference, higher throughput at large batch sizes, and reduced latency when generating long sequences. The improvement is most noticeable on memory-bandwidth-bound workloads like long-context inference.
How is RTX 5090 spot availability and where is it trending?
As of mid-2026, the RTX 5090 is available on Vast.ai and RunPod with growing inventory. Vast.ai shows offers starting at $0.39/hr from Canada, while RunPod lists at $1.17/hr. Supply is still ramping — the 5090 launched in early 2026 and gamer/creator inventory is beginning to enter rental markets. Expect spot prices to decline and availability to improve significantly through the second half of 2026.
What can 32 GB of VRAM do that 24 GB cannot?
32 GB enables FP16 inference for models in the 24-27B parameter range (Qwen 3.5 27B, Gemma 2 27B, Mistral Small 24B) without any quantization. On a 24 GB card these models require INT8 quantization, which reduces output quality — measurably so on tasks like code generation and mathematical reasoning where precision matters. 32 GB also provides substantially more KV cache space for long-context inference with smaller models.
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