ローカルLLM完全攻略:M2 Macで動かすAIシステム構築術:
SCR 588
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- Ollamaを入れたけれど、どのモデルを選べばいいか分からない。LM StudioやMLXとの違いも曖昧。Macのメモリでどこまで動くのか、API課金をどこまでローカルに逃がせるのか、自分の環境で判断したい。本書は、MacでローカルLLMを運用するための実践ガイドです。特定のモデルを一位に決める本ではありません。Ollama、LM Studio、MLXをどう使い分けるか。モデルサイズ、量子化、メモリ使用量、初回ロード、tokens/sec、回答品質をどう測るか。APIへ残す作業と、ローカルへ任せる作業をどう分けるか。そこを、自分のMacで再現できる形に落とし込みます。扱う内容:1. ローカルLLMに向く作業、APIへ残す作業2. Ollama / LM Studio / MLX の使い分け3. モデルサイズと量子化の見方4. Macで速度、メモリ、発熱、品質を測る手順5. 要約、分類、抽出、コード説明、長文下書きの評価表6. 失敗しやすいメモリ管理とモデル選定7. API併用の判断表と費用ログ8. 30日導入ロードマップ9. コマンド早見表、トラブル対応、モデル採用台帳ローカルLLMは、入れればすぐ万能になる道具ではありません。強いのは、要約、分類、抽出、下書き、ログ整理のように、毎日大量に発生する作業です。一方で、変化の速い情報、厳密な数値計算、最終判断、公開前レビューは、APIや人間に残したほうが安全な場面もあります。本書の目的は、流行りのモデル名を追うことではありません。自分のMacで測り、使える作業からローカル化し、API課金と作業負荷を少しずつ下げることです。MacでローカルLLMを試したい人、Ollamaを入れた後で止まっている人、ClaudeやChatGPTのAPI費用を見直したい人へ。
| Publisher | Independently published |
| Publication date | July 14, 2026 |
| Language | Japanese |
| Print length | 186 pages |
| ISBN-13 | 979-8187262977 |
| Item Weight | 340 g |
| Dimensions | 6 x 0.42 x 9 inches (15.2 x 1.1 x 22.9 cm) |
Product Description
ローカルLLM完全攻略:M2 Macで動かすAIシステム構築術: Ollamaのメモリ管理から量産パイプラインまで詰まりポイントを全解説
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紫陽花 響 Format: Paperback Editorial Review
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SCR 588
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