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.
Specs for every card we cover, each with its own detail page. Prices move, so check the current price at the retailer before you buy.
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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