Technology module
Memory hierarchy
As models grow, memory placement increasingly defines the product experience. VXI treats on-chip SRAM, cache, and external memory as one system and plans where weights, activations, and intermediate data live based on model behavior.
Memory · Dataflow · Efficiency
Why it matters
Co-design weights, activations, cache, and external memory around model behavior.
Placement driven by model behavior
The most frequently used data sits closest to compute, lowering access latency.
Balanced capacity and bandwidth
Strike the right balance between on-chip capacity, external bandwidth, and cost for models of different sizes.
Context management for generative AI
KV cache strategies for long contexts and multi-turn interaction keep responses consistent.
How it works
Requirement sizing
Estimate the memory needed for weights, activations, and context.
Tiered placement
Assign data to on-chip SRAM, cache, and external memory by access frequency.
Prefetch and reuse
Hide access latency with prefetching and data reuse strategies.
Continuous tuning
Refine placement with real workload feedback for both performance and stability.
What we align on during evaluation
Bring this information to your evaluation request so the VXI team can propose a concrete platform and adoption plan faster.
- Model size, context length, and concurrent usage scenarios
- External memory type and system cost targets
- Latency sensitivity and response consistency needs
- Capacity headroom for future model upgrades
Frequently asked questions
- How does the memory hierarchy shape the AI product experience?
- Memory placement determines how quickly data reaches compute. A good hierarchy lowers latency, reduces power, and keeps always-on products performing consistently.
- How can I estimate how much memory my model needs?
- Start with the workload planning tool on this site for a first estimate of weights and KV memory, then refine the placement with VXI engineers during evaluation.
- What challenges do long contexts bring?
- Long contexts grow the KV cache quickly and pressure both capacity and bandwidth. VXI uses tiered placement and context management to balance experience and cost.
Related technology modules
- SRAM-CIM compute path — Weight residency and near-memory compute that cut data movement
- Compiler and runtime — From model conversion and quantization to runtime control
- On-chip data fabric — Workload-aware data paths and module communication
Open the workload planner · What we align on during evaluation