A world-class, reusable memory characterization platform that turns real AI workload behavior into phase-tagged HBM4 stress signatures, SOA boundaries, and pre-failure guardrails — before customer deployment edge cases become escalations.
Workload diversity to expose dominant HBM4 stress dimensions while keeping DOE tractable, reviewable, and reproducible. Broader coverage extends using the same gold image and analysis pipeline.
Capacity + Bandwidth
Llama-3.1-70B-Instruct
Capacity and bandwidth stress from large-model inference; representative of practical customer serving workloads.
Primary stress signatures
Prefill burst
Decode steady state
Batch ramps
Concurrency changes
p95/p99 latency
HBM footprint
Throttle fraction
Memory temp slope
KV-Cache + Residency
Phi-3 128K (long context)
KV-cache and long-context stress; designed to isolate context length, memory headroom, and residency effects.
Primary stress signatures
Context length growth
KV-cache footprint
Prefill/decode split
Memory headroom
TTFT
p99 latency knee
HBM temp gradient
Bandwidth pressure
Bandwidth Baseline
STREAM
Controlled memory-bandwidth baseline for attribution; separates platform memory bandwidth and thermal response from LLM software behavior.
Primary stress signatures
Read/write/triad bandwidth
Sustained temp response
Throttle onset
Baseline headroom under power/cooling constraints
From “does it pass?” to “how does AI execution stress HBM4?”
A reusable platform that turns workload behavior into ranked stress mechanisms, SOA maps, and RCA-ready evidence.