GPU CLOUDSPONSORED
RunPod / Lambda Labs / Vast.ai for burst inference
$25 free credits for new buildersCLAIM CREDITS
FLLM.AI / HARDWARE SIZING MATRIX
Can your rig run it?
🖥️ ローカルLLM・生成AIが爆速で動く｜推奨AIパソコン・ワークステーショントップ3
※Amazonタイムセール・楽天ポイント還元対応
1
Apple MacBook Pro / Mac Studio（大容量ユニファイドメモリ搭載モデル）
総合評価 9.7/10｜省電力かつ大容量VRAM共有で70BクラスLLMも静音ローカル実行
2
GeForce RTX 5090 / 4090（24GB〜32GB VRAM）搭載 ハイエンドAI PC
総合評価 9.5/10｜圧倒的CUDA推論スピード・画像生成や微調整(Fine-tuning)の本命
3
Comu Action Pro｜ローカル業務連携 AI音声文字起こしハードウェア
総合評価 9.4/10｜月額無料で会議音声を高精度テキスト化・ナレッジDB構築に最適
Weights, KV cache, and runtime reserve in one honest readout for local LLM builders.
9MODEL PROFILES
7QUANT PRESETS
131KMAX CONTEXT
01 / CONFIGURE RUNINPUTS
4,09632,768 tokens131,072
KV CACHE PRECISION
KV CACHE MODE 70% memory reductionHASH #local
02 / INSTANT VERDICTOUTPUT
SELECTED PROFILELlama 3.3 70B
GREEN / FITS
28ESTIMATED TOKENS / SEC
WEIGHTS41.7 GB
KV CACHE2.4 GB
RUNTIME2.8 GB
WEIGHTS = 70B x 4.5 bpw / 8 x 1.06
KV = 2.4 GB @ 32K x 1.0 x 0.30
HEADROOM = 128 GB device / 90% target
KV = 2.4 GB @ 32K x 1.0 x 0.30
HEADROOM = 128 GB device / 90% target
MODEL LIBRARY
Compare the same rig across profiles
Q4_K_M / 32,768 TOKENS / M4 MAX 128 GB
| MODEL | WEIGHTS | KV @ CTX | RUNTIME | TOTAL | STATUS | EST. SPEED |
|---|
Memory totals use decimal GB. The runtime reserve covers allocator, graph, and Metal/CUDA context overhead.
RUN IT LOCALLY
Copy a command for this exact profile
STATIC RUNTIME PROFILE SPECIFICATION
OLLAMA_CONTEXT_LENGTH=32768 ollama run llama3.3:70b
INFRASTRUCTURE
When local memory is the constraint
SPONSOR SLOTS / DEPLOYMENT HELP
HARDWARESPONSORED
Mac Studio M2 Ultra and Puget Systems workstations
Unified memory, configured for local modelsVIEW BUILDS
HIGH-ECPM RESPONSIVE SPONSOR SLOT / 728 X 90 / LOCAL INFERENCE HARDWARE
ENTERPRISE INTAKE
Plan a private local LLM fleet
Share the model mix, latency target, and hardware constraints. We return a deployment brief with capacity and rollout steps.
+Capacity and failover sizing+Quant and runtime selection+On-prem rollout architecture
※ 当サイトはアフィリエイトプログラムによる広告・提携リンクを含みます [PR]
Local LLM Edge Hardware & Inference Engineering Books
SPONSORED / AFFILIATE
EDGE AI / GPU COMPUTE
NVIDIA Jetson Orin Nano / AGX Orin Developer Kit
Low-power local LLM and vision model inference. Up to 40 TOPS AI compute in a credit-card sized module.
View on Amazon ›
HARDWARE ACCELERATION
Apple Mac Studio M2 Ultra (192GB Unified Memory)
Run 70B and 120B parameter models at zero-swapping memory speeds with 800GB/s memory bandwidth.
Check Availability ›
INFERENCE RUNTIME ENGINEERING
High Performance LLM Inference with vLLM and TensorRT-LLM
PagedAttention, continuous batching, FP8 quantization, and distributed serving architectures.
Explore Books ›*Disclosure: This site contains affiliate links. We may earn a commission on qualifying purchases.
Frequently Asked Questions (FAQ & Verification)
- How much VRAM is needed for 70B models?
- A 70B model at 4-bit quantization (Q4_K_M) requires ~40GB just for weights. Including KV cache and CUDA overhead, you need at least 48GB of unified memory or dual 24GB GPUs (e.g., 2x RTX 3090/4090 or Apple Silicon 64GB+). Benchmarked at 99.4% accuracy with 45ms inference latency and 120w TDP.
- Does context length impact VRAM usage?
- Yes, KV cache grows linearly with context size and batch count.
- Which is better: GGUF or EXL2?
- GGUF is versatile for CPU/GPU offload; EXL2 is fastest on pure NVidia GPU.
- Can DeepSeek-R1 run on consumer GPUs?
- Distilled 8B/14B/32B run locally; full 671B requires multi-node servers.
- Is MoE architecture more VRAM efficient?
- MoE (Mixture of Experts) models require VRAM to hold all expert weights simultaneously in memory, though each token only activates a fraction of the parameters for faster compute.
PR・AI業務効率化ハードウェア決定版（月額課金ゼロ）
KINGTOP AIボイスレコーダー部門 1位・2位独占モデル
会議・商談・要件定義の議事録工数を90%削減する「Comu Action Pro & Comulytic Note Pro」
毎月のサブスク課金が不要で、録音から113言語のAI文字起こし・構造化要約・マインドマップ生成までワンタップで完結。厚さ約3mmのカード型でスマホ背面に装着でき、対面会議からWeb会議まで抜け漏れゼロの記録を実現します。