RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
RUNLOCALAI

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OP·Fredoline Eruo
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Some links on this site are affiliate links (Amazon Associates and other first-class retailers). When you buy through them, we earn a small commission at no extra cost to you. Affiliate links do not influence our verdicts — there are cards we rate highly that we don't have affiliate relationships with, and cards that sell well that we refuse to recommend. Read more →

SYS · ONLINEUPTIME · 100%2026 · operator-owned
RUNLOCALAI · v38
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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
⚠Total VRAM ≠ usable VRAM

The single most important rule when reading multi-GPU specs: total VRAM is not pooled VRAM. Two 24 GB cards do NOT give you 48 GB to load a single model into. Each card holds its share of the model via tensor or pipeline parallelism, and runtime overhead eats per-card VRAM. Only Apple unified memory and NVLink-Switch fabrics genuinely pool. Every combo below shows total vs effective with the honest explanation.

Filter
Topology
AnySingle-node multi-GPUApple unifiedApple clusterMixed GPUDistributed
Difficulty
AnyBeginnerIntermediateAdvancedExpert
Interconnect
AnyPCIeNVLinkNVLink-SwitchThunderboltUnified
Effective VRAM
Any40+ GB80+ GB140+ GB

Combinations (0)

Each combo links to operator-grade detail with topology diagram, runtime compatibility matrix, failure modes, and recommended models.

No combinations match this filter combination. Try a less restrictive filter.

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.