An engineering practice specialised in the low-level work that platforms don't do for you: LLM, VLM, speech, computer vision and forecasting models — benchmarked, profiled and optimised down to the kernel, and served to many concurrent users at low latency. On your hardware or ours. We don't promise numbers. We measure them.
Scoped, measurable engagements. Every recommendation is backed by profiler data, not vibes.
Benchmark and tune the path that serves your model — across llama.cpp/GGUF, ONNX Runtime, TensorRT-LLM, CUDA, Vulkan and Metal. Quantization trade-offs (Q4/Q5/Q8/FP16), prefill vs decode, KV-cache pressure, context scaling and CPU/GPU offload.
A serving layer where one warm model handles many client sessions with streamed output. Continuous/in-flight batching, KV-cache slot management, admission control, cancellation — built to hold stable TTFT and inter-token latency under load.
Find out where your inference time and hardware budget actually go. We instrument workloads with Nsight, runtime telemetry and load tests to pin CPU, GPU, memory, scheduler, KV-cache and kernel bottlenecks — before touching a line of code.
When runtime tuning isn't enough, we adapt the model — and optimize the pipeline for memory, speed and cost. We assess your data and labels, define eval sets and acceptance metrics, and apply parameter-efficient methods where they fit.
Recommendations based on measured workload behavior, not hardware assumptions. Single- and multi-GPU experiments, VRAM capacity planning, and platform-specific optimization for NVIDIA, Vulkan-capable devices and Apple silicon — run on your infrastructure, or on ours when an independent proof-of-concept is faster.
Most teams are overpaying for GPUs or shipping latency they could halve — they just can't see where. Start here: a fixed-price, fixed-timeline audit that finds it.
We pick tools by what the profiler says, not by fashion.
You talk to the people writing the kernels — not an account manager. No juniors, no layers.
Low-latency inference and rendering specialists. Production speech-to-speech and LLM pipelines (streaming ASR → vLLM → TTS), GEMM/kernel optimization on H100-class hardware, mixed precision and inline-PTX work — driven end-to-end by profiler data.
Low-level multimedia and computer-vision engineering with strong optimization-theory foundations. Realtime translation and TTS services, hardware-accelerated WebRTC streaming (NVENC/NVDEC), and ONNX-based detection tuned for production latency budgets.
Describe your model, hardware target and the metric that's keeping you up at night. We'll come back with a scope and a fixed quote — usually within a couple of days.