Inference & Serving | ScaleMindLabs

Inference & Serving

Latency, throughput, batching, caching, and deployment architecture.

May 10, 2026•Inference & Serving

When FP8 KV Cache Speeds Up Decode and When It Only Saves Memory

How FP8 KV cache affects real LLM serving latency: storage-only paths, fused dequantization, FP8 attention, decode break-even points, and calibration risk.

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May 3, 2026•Inference & Serving

When Long Context Makes KV Cache Quantization Worth It: FP8, INT4, and Scale Budgets

How FP8 and INT4 KV cache quantization trade memory headroom for scale calibration, key/value asymmetry, residual windows, and long-context quality checks.

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Mar 14, 2026•Inference & Serving

KV Cache Compression in Practice: FP8/INT4 Trade-offs, Paging, and Attention Accuracy Drift

A systems-level analysis of KV cache compression, paging behavior, and quality drift under FP8/INT4 serving regimes.

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