Memory-Mapping
Zero DRAM Bottlenecks
Optimizing LLM weights for Cerebras CS-3 SRAM to achieve purely deterministic compute velocity by eliminating external memory dependencies.
SRAM Mapping
Direct allocation of model parameters into the 44GB on-chip SRAM of the Wafer-Scale Engine.
Zero DRAM Logics
Bypassing HBM/DRAM latency entirely to enable ultra-fast token generation at lightspeed.
SwarmX Optimization
Orchestrating the streaming of trillion-parameter models via high-bandwidth interconnect fabrics.
21 PB/s Throughput
Leveraging massive core-to-memory bandwidth for real-time inference on the CS-3 system.
Process Logic: SRAM-Centric Engineering
| Phase | Action (Memory Mapping) | Outcome (Compute Velocity) |
|---|---|---|
| **Partitioning** | Segmenting LLM weights for distributed on-wafer SRAM allocation. | Minimized physical distance between compute and data. |
| **Offloading** | Eliminating off-chip memory fetch cycles for compute kernels. | 100% deterministic latency; zero jitter in token production. |
| **Validation** | Benchmarking trillion-parameter throughput without HBM overhead. | Sustained peak-performance for sovereign AI infrastructures. |
Malgukke Insight: The End of HBM Dependency
Während herkömmliche Cluster an der **Memory Wall** scheitern, setzen wir auf radikales **SRAM-Mapping**. Durch die Nutzung der 44GB On-Chip Kapazität der **Cerebras CS-3** eliminieren wir den DRAM-Flaschenhals. Dies ist der technologische Wendepunkt, um Inferenzgeschwindigkeiten zu erreichen, die auf Standard-Architekturen physikalisch unmöglich sind.