Backend.AI + Intel Arc Pro B70:
Memory for Agentic AI

For agentic AI workloads, memory has become the spec that decides how useful a card actually is. The KV cache an inference engine keeps to reuse earlier computation grows with context length and concurrency; coding agents repeatedly reference a long base context across turns, and production services run dozens of such agents at once. When memory runs short, the engine evicts cache and recomputes, and throughput drops sharply. Alongside offloading cache out of the GPU, starting with a card that carries more memory pushes that threshold further out.

Intel Arc Pro B70 is a workstation GPU built on the Xe2 (Battlemage) architecture, with 32GB of GDDR6 (ECC) memory, 608GB/s of bandwidth, and a 230W board power budget. Since the memory left after loading model weights is what holds KV cache, a card with more headroom sustains longer contexts and more concurrent requests without eviction. It lets you prototype long-context agents on a local workstation without renting datacenter capacity, while keeping the same model serving stack you run in production.

The brief includes comparison benchmarks against a workstation GPU of the same class. Measured with vLLM across three open models while varying input length, output length, and concurrency, it shows how memory capacity reshapes the throughput curve as agentic load deepens. See the full brief for the measurement conditions, per-model results, and decision criteria by workload.