Multi-GPU decision intelligence
Hardware combinations for local AI
Dual GPUs, quad GPUs, mixed cards, Apple unified memory, Exo clusters, distributed serving. The honest answer to “what hardware combination should I build to run this model well?” — with effective-VRAM math, runtime compatibility, failure modes, and who should avoid each setup.
By Fredoline Eruo · Updated continuously
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Combinations (2)
Each combo links to operator-grade detail with topology diagram, runtime compatibility matrix, failure modes, and recommended models.
Dual RTX 3090 (24 GB × 2)
The reference dual-GPU local-AI rig. NVLink optional. 48 GB total / ~46 GB effective with tensor parallelism. The cheapest path to 70B-class models at 2025-2026 prices.
Single-node multi-GPUNVLinkintermediate
VRAM 46/48 GB
Power 700W
Dual RTX 4090 (24 GB × 2)
Two consumer-flagship cards. PCIe 4.0 only — no NVLink on 4090. 48 GB total / ~45 GB effective with tensor parallelism. ~30% faster decode than dual 3090 at 2× the cost.
Single-node multi-GPUPCIeintermediate
VRAM 45/48 GB
Power 900W
Going deeper
- Running local AI on multiple GPUs in 2026 — the flagship buying / deployment guide.
- Distributed inference systems — architectural depth on tensor / pipeline / expert routing.
- Execution stacks — full deployment recipes that pair combos with runtimes and models.
- Hardware catalog — single-GPU baselines that the combos here build on.