Fig. 3.0 · Technology
Memory,
orchestrated.
We build the API, the runtime and the memory tier, and drive the best open engines underneath. One system, end to end.
[ Memory ]
Tiered memory orchestration.
AI is not too big for the box. Most AI tooling just assumes everything fits in one place.
We track every tensor's access pattern. Hot weights stay in GPU VRAM. Warm weights move to system RAM and return when called. The cache is tiered the same way.
This is not quantization. Quantization reduces precision. We reduce residency. A 70B model at FP16 needs 140GB of VRAM. The two stack: tiered and quantised, 70B runs on a single 24GB card, 43 of 80 layers on the GPU.
The trade-off is latency, not accuracy. First-token latency increases when a layer needs to be pulled from system RAM. But once warm, throughput is governed by the GPU in the box, not the one you wish you had.
[ Stack ]
The whole stack. One vendor.
Everything from the API your application calls to how weights move through memory. The only thing you bring is the metal.
Fig 3.2 · Stack
API
openai-compatibleDrop-in for the OpenAI endpoint.
On the machine
orchestrationProbes the hardware, places the workload, serves traffic.
Memory orchestration
Weights tiered across VRAM and system RAM.
Engine
swappableOpen-source engines, picked per workload.
Compute
any machineGPU, CPU, TPU or mixed.
One vendor
API, runtime, memory tier, engine integration. All ours.
Open underneath
We integrate the best engines. You inherit upgrades.
Any machine
The only piece we do not own is the hardware.
[ Comparison ]
Engines are parts. This is the system.
You can. But each solves one problem well and leaves the rest to you.
llama.cpp alone
Excellent for single-node CPU inference. No fleet management, no memory tiering beyond mmap, no hot-swap. You build the orchestration layer yourself.
vLLM alone
Best throughput on NVIDIA GPUs with PagedAttention. No CPU tiering by default, no fleet control plane. You run one model per server.
TensorRT-LLM alone
Fastest on NVIDIA when you have time to compile. No dynamic engine switching. You optimize once per GPU type.
See it run on your workload.
Bring your stack. We will benchmark Sector88 against it on your workload, at your site.