16GB
VRAM
GDDR7 memory
AIVory · GPU Marketplace
Live spot pricingNVIDIA RTX 5080 — Blackwell speed on a 16 GB budget.
The RTX 5080 brings Blackwell tensor cores and 960 GB/s GDDR7 bandwidth to the 16 GB tier. At $0.39/hr on spot, it's the fastest consumer card for serving 7B-12B models — delivering more tokens per second than the RTX 4090 on small models thanks to GDDR7's raw throughput, despite having less total VRAM.
At a glance
RTX 5080 specifications.
Key hardware specs that determine what workloads this GPU handles.
960 GB/s
Memory Bandwidth
peak throughput
360W
TDP
thermal design power
Blackwell
Architecture
NVIDIA GPU architecture
Spot pricing
RTX 5080: 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 5080.
Tested model-GPU pairings with notes on why each is a good fit.
Mistral Nemo 12B
12B in INT8 at ~12 GB leaves 4 GB for KV cache. The 960 GB/s bandwidth generates tokens faster than any other 16 GB card — including the RTX 4090's 1.01 TB/s, because Blackwell tensor cores are more efficient.
View model pricing →
Llama 3.2 8B
8B in FP16 at ~16 GB uses the full VRAM. For maximum throughput, INT8 at ~8 GB leaves half the memory free for batched serving at high concurrency.
View model pricing →
Gemma 2 9B
9B in FP16 needs ~18 GB — requires INT8 quantization on the 16 GB card. At ~9 GB in INT8, the Blackwell FP8 tensor cores can push throughput even higher.
View model pricing →
Use cases
What the RTX 5080 is built for.
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Fastest token generation for 7B-12B models
On models that fit in 16 GB, the RTX 5080's Blackwell tensor cores and 960 GB/s GDDR7 make it the fastest consumer GPU for inference. It outperforms the RTX 4080 ($0.27/hr, 717 GB/s) and matches the RTX 4090 ($0.29/hr, 1.01 TB/s) on throughput while adding FP4/FP8 support that the Ada generation lacks.
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Cost-effective Blackwell architecture access
At $0.39/hr, the RTX 5080 is the cheapest Blackwell GPU on the spot market — the same price as the consumer RTX 5090 at minimum, but with wider availability. For teams evaluating Blackwell's FP4/FP8 capabilities or optimizing models for the new architecture, the RTX 5080 provides a low-cost test bed.
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Gaming-GPU-to-inference conversion for hobbyists
The RTX 5080 is primarily a gaming card, and many spot instances come from consumer hardware being rented for compute. For individual developers and hobbyists who want to experiment with LLM inference without the overhead of enterprise GPU pricing, the RTX 5080 offers mainstream accessibility at mainstream pricing.
FAQ
Common questions.
RTX 5080 vs RTX 4080 — is the upgrade worth it?
The RTX 5080 costs $0.39/hr vs $0.27/hr for the RTX 4080. Both have 16 GB VRAM, but the RTX 5080 has 34% more bandwidth (960 vs 717 GB/s) and Blackwell tensor cores with FP4/FP8 support. If your bottleneck is token generation speed on 7B-12B models, the RTX 5080 delivers 30-40% more throughput. If you're just running small models and want the lowest hourly rate, the RTX 4080 is the better value.
RTX 5080 vs RTX 5090 — both Blackwell, why not just get the 5090?
The RTX 5090 has 32 GB GDDR7 at 1.79 TB/s bandwidth and starts at $0.39/hr too. If both are the same spot price, the RTX 5090 is strictly better — more VRAM and more bandwidth. But the RTX 5090 is newer and less available on spot markets, while the RTX 5080 has broader supply. When the RTX 5090 is available at parity pricing, choose it. When it's not, the RTX 5080 gets you Blackwell today.
Is 16 GB still enough VRAM in 2026?
For inference on 3B-12B models, 16 GB is practical. Mistral 7B in FP16, Gemma 2 9B in INT8, and Nemo 12B in INT8 all fit. For 14B+ models, you need 20-24 GB. The 16 GB tier is the entry point for GPU inference — capable for small models, insufficient for medium ones. If you're not sure, start here and upgrade if your model outgrows the VRAM.
Does the RTX 5080 support multi-GPU setups?
Consumer Blackwell GPUs do not support NVLink. Multi-GPU RTX 5080 setups rely on PCIe bandwidth (~32 GB/s bidirectional), which is too slow for tensor-parallel inference on sharded models. Use multiple RTX 5080s for independent model instances (one model per card), not for splitting a single large model across cards.
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