8GB
VRAM
GDDR6 memory
AIVory · GPU Marketplace
Live spot pricingNVIDIA RTX 3070 — the absolute floor price for tensor-core inference.
Eight gigabytes of GDDR6 and Ampere tensor cores at $0.13/hr. The RTX 3070 is the cheapest way to get NVIDIA tensor core acceleration on the spot market — period. VRAM constrains you to 3B-7B models in INT4 or sub-3B models in FP16, but for those workloads you simply won't find a lower hourly rate.
At a glance
RTX 3070 specifications.
Key hardware specs that determine what workloads this GPU handles.
448 GB/s
Memory Bandwidth
peak throughput
220W
TDP
thermal design power
Ampere
Architecture
NVIDIA GPU architecture
Spot pricing
RTX 3070: 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 3070.
Tested model-GPU pairings with notes on why each is a good fit.
Llama 3.2 3B
3B in FP16 at 6 GB leaves 2 GB for KV cache. The best model experience on the RTX 3070 — large enough to be useful, small enough to run without quantization.
View model pricing →
Gemma 2 2B
2B in FP16 at 4 GB leaves 4 GB free. Run at high concurrency or host alongside an embedding model for a self-contained RAG pipeline on one cheap card.
View model pricing →
Phi-3 Mini 3.8B
3.8B in FP16 needs 7.6 GB — a tight fit with minimal KV cache room. INT8 at ~3.8 GB gives 4.2 GB headroom and is the recommended quantization for this card.
View model pricing →
Use cases
What the RTX 3070 is built for.
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Lowest-cost entry point for GPU-accelerated inference
At $0.13/hr, the RTX 3070 costs $3.12/day for 24-hour GPU access. No other tensor-core GPU comes close — the next cheapest options (RTX 3090 at $0.10/hr) have more VRAM but aren't always available. For hobbyists, students, and early-stage startups experimenting with self-hosted inference for the first time, the RTX 3070 removes the cost barrier entirely.
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Lightweight embedding and classification pipelines
Text embedding models (E5, BGE, nomic-embed) and classification models (DistilBERT, DeBERTa) typically use 0.5-2 GB of VRAM. The RTX 3070's 8 GB handles these with ample headroom, and the 448 GB/s bandwidth keeps throughput high for batch processing. At $0.13/hr, embedding a million documents costs pennies.
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CI/CD GPU testing on real hardware
Automated test suites that validate GPU inference pipelines need the cheapest possible GPU instance to keep CI costs low. The RTX 3070 at $0.13/hr runs your inference tests on real Ampere hardware — verifying CUDA compatibility, tensor core utilization, and model loading — for a fraction of what production GPUs cost.
FAQ
Common questions.
Is 8 GB enough VRAM for any useful LLM?
Yes, for small models. Llama 3.2 3B in FP16 needs 6 GB. Gemma 2 2B needs 4 GB. Phi-3 Mini 3.8B needs 7.6 GB in FP16 (tight) or 3.8 GB in INT8 (comfortable). Mistral 7B requires INT4 quantization to fit (~3.5 GB), with noticeable quality loss. The RTX 3070 is best for sub-4B models in FP16 or 7B models in INT4 when quality is secondary to cost.
RTX 3070 vs RTX 3080 — worth the extra $0.04/hr?
The RTX 3080 ($0.17/hr) adds 2-4 GB of VRAM and 69% more bandwidth (760 vs 448 GB/s). For 7B models, the extra VRAM makes INT8 quantization practical instead of requiring INT4. For 3B models, both cards work but the 3080 generates tokens 40-50% faster. If you're running anything beyond 3B models, the RTX 3080 is worth the $0.96/day premium.
Can the RTX 3070 run Mistral 7B?
Only in INT4 quantization, which needs ~3.5 GB. This leaves 4.5 GB for KV cache, which is workable. However, INT4 Mistral 7B on the RTX 3070 produces noticeably lower quality output than INT8 or FP16 on a card with more VRAM. If Mistral 7B is your target model, the RTX 3090 at $0.10/hr is actually cheaper AND gives you 24 GB for INT8 serving.
What about VRAM for Stable Diffusion?
Stable Diffusion 1.5 runs on 8 GB, but slowly — limited batch size, no high-resolution upscaling. SDXL requires 10+ GB and won't fit. For image generation, the RTX 3080 (10-12 GB) or RTX 4090 (24 GB) are more practical. The RTX 3070 handles SD 1.5 for experimentation but is frustratingly slow for production image generation.
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