Memory Offload & aiDAPTIV+
Extending GPU memory onto NVMe to fine-tune larger models.
3 articles
Sizing guide 18 Aug 2026
Sizing On-Prem AI by Model Class: From 3B Laptops to 671B Servers
Pick the machine by the model class you intend to run and fine-tune, not by GPU brand. A tiered reference range from a 3B laptop to a 671B eight-GPU server,…
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Concept 18 Aug 2026
KV-Cache Reuse and TTFT: Why Inference Latency Gets Unstable at Scale
Time-to-first-token degrades under concurrency when the KV cache is evicted and recomputed. Published aiDAPTIV+ figures show average TTFT falling from 250 ms to 78 ms with cache reuse, and variable…
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Explainer 18 Aug 2026
aiDAPTIV+ Explained: Extending GPU Memory onto NVMe for On-Prem AI
GPU memory, not compute, is what stops most teams fine-tuning large models on hardware they own. aiDAPTIV+ extends GPU memory onto high-endurance NVMe so a workstation or server can train…
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