Boundary Labs is an independent research operation focused on persistent agent memory, local inference optimization, and production-grade autonomous systems on constrained hardware. The work is already live, measured, and publicly documented. Additional compute would not create direction; it would accelerate an existing agenda with defined bottlenecks and concrete outputs.
The lab already operates as a real system rather than a proposal. One tier handles orchestration, agents, monitoring, and web services; another is dedicated inference infrastructure. Results are generated on live hardware, published publicly, and tied to specific deployment constraints rather than idealized benchmarks.
Adam Selene and Mike provide the memory-architecture side of the lab: long-running behavior, continuity across backend changes, nightly consolidation, and evaluation against memory-specific benchmarks including the current MESA public release.
Blackwell consumer-GPU work across `vLLM`, `llama.cpp`, TRT-LLM constraints, NVFP4 behavior, PCIe tensor parallelism, and stack-specific failure modes under production conditions.
Services are not only benchmarked but run continuously. Recovery, boot hardening, watchdogs, and topology-aware routing are part of the research surface rather than an afterthought.
Most public AI evaluation still centers on model quality in isolation. Boundary Labs focuses on the layer that determines whether a model can function as part of a durable system: memory architecture, inference deployment, runtime continuity, and cost-realistic operation on accessible hardware.
The output is useful beyond this lab because it produces operational knowledge that other independent labs, small teams, and applied researchers can reuse: what works on consumer Blackwell, where current stacks break, which optimizations are real, and what architectural patterns preserve agent continuity across changing model substrates.
| Constraint | Current effect | What it limits |
|---|---|---|
| GPU memory ceiling | 32 GB total on the current dual-GPU tower | Larger model sweeps, wider quantization matrices, and replication across higher-context deployments |
| Single-node scope | Most experiments are validated on one local inference cluster | Cross-environment replication and stronger claims about portability |
| Throughput budget for evaluation | Long-form eval suites like the 361-item MESA gold set consume meaningful wall-clock time on local hardware | Larger benchmark matrices, longer longitudinal studies, and more frequent regression testing |
| Cloud-scale comparison gap | Excellent local numbers, limited systematic cloud-side contrast | More complete deployment guidance across local-first and elastic compute tiers |
| Window | Output | Form |
|---|---|---|
| 30 days | Expanded compatibility and optimization findings for the active inference stack | Public benchmark update, technical notes, reproducible configs |
| 60 days | Cross-model or cross-environment evaluation set with documented bottlenecks | Artifact release, benchmark tables, implementation notes |
| 90 days | Publication-grade synthesis of memory, inference, and deployment findings | Preprint, public benchmark corpus, deployment guide |
Boundary Labs is not presenting a blank-slate idea in search of resources. The core posture is different: the systems already run, the benchmark record already exists, and the public output is already underway. Additional compute would improve the depth, speed, and portability of that work.
The best fit is support that values transparent technical reporting, operational realism, and open artifacts over inflated claims. The lab is optimized for applied research output: measurements, deployment findings, benchmark corpora, and architecture notes that other operators can actually use.
Boundary Labs is available for research partnerships, compute-backed collaboration, and infrastructure-oriented sponsorship aligned with open technical output.