Dual RTX 3090 vs single RTX 5090 — which one for local AI?
The answer
One paragraph. No hedging beyond what the data actually warrants.
For 70B-class models: dual 3090. For everything else: single 5090.
Dual RTX 3090 gives you 48GB VRAM total for ~$1,200 used. Single RTX 5090 gives you 32GB for ~$1,999. The 3090 path wins on raw VRAM per dollar and on running models that don't fit in 32GB (Llama 3.3 70B Q4_K_M at full context needs 42-48GB).
The 5090 path wins everywhere else: single-card software simplicity (no tensor parallelism), lower TDP (575W vs 700W combined), better FP4/FP8 perf for the new generation of NVFP4-quantized models, warranty + new condition.
Real-world friction points with dual 3090s:
- Motherboard PCIe lanes: most consumer boards split x16 → x8/x8 across two slots.
- PSU sizing: 1000W minimum, 1200W comfortable.
- Case airflow: stacking 3-slot cards creates a thermal sandwich.
- vLLM tensor parallelism works fine; Ollama's multi-GPU support is the rougher path.
Decision rule: if your daily workload includes 70B models, dual 3090 is the leverage pick. If you're running 32B-class or below, the 5090 simplicity premium is worth $800.
Explore the numbers for your specific stack
Where we got the numbers
TPS estimates from bandwidth math (936 GB/s × 2 for dual 3090 vs 1792 GB/s for 5090) + community runlocalai-bench submissions on /community. TDP from NVIDIA spec sheets.
Also see
Verdict, benchmarks, hardware guidance, beginner mistakes.
The used-market workhorse. What to inspect when buying used.
Current prices for both cards across US/EU/UK/CA/AU stores.
The opinionated software stack: vLLM, tensor parallelism, the motherboard + PSU build.
Other questions in this thread
Other /q/ landings on the same topic — same editorial discipline.
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