Local LLM Dual GPU Setup: How Much Does It Actually Improve Performance?

The author tested running large language models locally with a dual GPU setup (RTX PRO 4500 Blackwell + RTX 4000 SFF Ada, totaling 52GB VRAM) using LM Studio to run gpt-oss-120b.

Results show dual GPU significantly improves performance for long contexts and large models. Detailed environment setup, testing methodology, and performance data provided — valuable for developers wanting to run large models locally.

ローカルLLM で 2 GPU 構成を検証してみる

ローカル環境で大規模言語モデル（LLM）を動かす際、GPUを2枚構成にすることで性能が向上するのか検証してみました。

CPU: AMD Ryzen 9 5900XT

NVIDIA RTX PRO 4500 Blackwell (VRAM 32GB)

NVIDIA RTX 4000 SFF Ada (VRAM 20GB)

使用ツール:LM Studio (gpt-oss-120bをローカルで実行)

プロンプト: 3000語程度のショートストーリーを生成してください。

単体GPUと2GPU構成での性能差（tokens/sec）を測定

Ctrl + Shift + H で Hardware を表示

GPUs に検出された GPU の有効/無効を切り替えるトグルスイッチがある

2GPU構成におけるメリットは「より多くGPUにオフロードできる」ことに尽きるようです。

ただし、オフロード数が増えても通信オーバーヘッドや非対称GPU構成による遅延が生じるため、性能向上にはつながらない（むしろ低下する）ケースも多いと考えられます。

フリーランスのフロントエンドエンジニア | TypeScriptを中心にWebアプリ開発を担当。現在はRustを学習しつつ、AI活用やローカルLLM・ComfyUIを試しています。

In-Depth Analysis and Industry Outlook

From a broader perspective, this development reflects the accelerating trend of AI technology transitioning from laboratories to industrial applications. Industry analysts widely agree that 2026 will be a pivotal year for AI commercialization. On the technical front, large model inference efficiency continues to improve while deployment costs decline, enabling more SMEs to access advanced AI capabilities. On the market front, enterprise expectations for AI investment returns are shifting from long-term strategic value to short-term quantifiable gains.

However, the rapid proliferation of AI also brings new challenges: increasing complexity of data privacy protection, growing demands for AI decision transparency, and difficulties in cross-border AI governance coordination. Regulatory authorities across multiple countries are closely monitoring these developments, attempting to balance innovation promotion with risk prevention. For investors, identifying AI companies with truly sustainable competitive advantages has become increasingly critical as the market transitions from hype to value validation.

From a supply chain perspective, the upstream infrastructure layer is experiencing consolidation and restructuring, with leading companies expanding competitive barriers through vertical integration. The midstream platform layer sees a flourishing open-source ecosystem that lowers barriers to AI application development. The downstream application layer shows accelerating AI penetration across traditional industries including finance, healthcare, education, and manufacturing.

Additionally, talent competition has become a critical bottleneck for AI industry development. The global war for top AI researchers is intensifying, with governments worldwide introducing policies to attract AI talent. Industry-academia collaborative innovation models are being promoted globally, with the potential to accelerate the industrialization of AI technology.