Tools, frameworks, and models for local AI
12 resources found for "Inferenza"
Apple Silicon chip with exceptional performance for mobile development and edge inference.
Il Mac più potente di Apple: M5 Ultra, primo chip a quattro die (due M5 Max uniti da UltraFusion), fino a 36 core CPU, 80 core GPU e 512GB di memoria unificata a 1,2 TB/s. Pensato per modelli frontier-class in locale: MoE da centinaia di miliardi di parametri e RAG su corpora enormi, senza cloud. Il clustering RDMA di più unità porta fino a 3× l'inferenza distribuita.
80GB HBM2e datacenter GPU: the standard for datacenter training and inference. Requires dedicated servers and enterprise power.
The top-tier datacenter GPU with 80GB HBM3: the top-of-the-line for large-model training and inference. Enterprise configurations only.
The most accessible consumer GPU with 12GB VRAM: the entry point for local inference. Comfortably runs 7-8B quantized models.
16GB VRAM at an entry-level price: the right choice for quantized 13-14B models. Good headroom for personal use.
16GB VRAM with 672 GB/s bandwidth: high-end performance at a contained price. Among the best VRAM/price ratios.
The world's most powerful consumer GPU for local training and high-speed inference.
Blackwell GB205 di fascia media: 12GB GDDR7 su bus 192-bit (672 GB/s), 6.144 CUDA core, 250W. Supporto completo ai formati FP4/FP8 per l'inferenza. Lancio a 549 USD, marzo 2025.
16GB GDDR7 with 960 GB/s bandwidth: the mid-range Blackwell GPU. Decode speed well above prior generations.
The fastest consumer GPU on the planet: 32GB GDDR7 and 1,792 GB/s bandwidth. The top pick for local inference without going to the datacenter.
Single-board computer with an 8-core ARM Cortex-A76 CPU and 2–16 GB of RAM: the reference for learning and running small local AI models in CPU inference.
