Co-Designing Intelligence, Memory & Silicon
Running an interactive autonomous coding agent locally is fundamentally a memory bandwidth challenge. Storing model weights is trivial; streaming active parameters at 20+ tokens per second on quiet personal hardware requires original engineering.
Solving the Memory Bandwidth Bottleneck
Running frontier intelligence locally is fundamentally an engineering challenge of memory bandwidth, thermal dissipation, and hardware accessibility.
The Memory Bandwidth Wall
Large-scale autoregressive models require reading active parameter weights for every generated token. Frontier intelligence at interactive speeds requires hundreds of gigabytes per second of sustained throughput.
Sparse MoE Working Set Locality
Mixture-of-Experts models activate only a fraction of their total parameters per token. In software engineering workloads, expert activations exhibit strong temporal and domain locality.
Tiered Memory Topology
Rather than relying on monolithic, expensive HBM silicon, our architecture evaluates hybrid configurations combining ultra-fast accelerator RAM, high-capacity system memory, and fast NVMe cache.
Task-Aware Quantization
Software engineering requires exact syntax, logical coherence, and long-range symbol tracking. Extreme uniform quantization often degrades multi-file reasoning capabilities.
Evaluating Physical Topologies
Each memory topology presents fundamental physical tradeoffs between sustained throughput, power consumption, thermal design, and manufacturing economics.
Highly accessible, but PCIe bus bandwidth severely bottlenecks uncached multi-billion parameter autoregressive token decode to under 1 token/sec.
Capable of holding large model checkpoints in RAM, but higher thermal dissipation and acoustic fan profiles make it unsuitable for quiet personal desktop environments.
Pairing high-bandwidth memory with dynamic sparse MoE routing allows interactive token throughput while preserving consumer power and thermal boundaries.
Provides extreme throughput but requires thousands of watts of cooling and multi-thousand euro enterprise silicon, violating our accessibility principle.
Research Collaboration
We collaborate with hardware engineers, semiconductor specialists, and AI researchers working on efficient local inference.
Get in touch with our team