Pick from any open-source model. We deploy it, fine-tune it, and run it on your cloud, on-prem stack, or edge hardware - including the new Nvidia DGX Spark. Twenty-two of those models are running on our own desk right now.
We help engineering teams move past API wrappers. We deploy, train, and configure high-performance, private, custom models inside your infrastructure.
We take any open-source model and make it production-ready: specialised inference servers, autoscaling, structured monitoring, and custom guardrails.
Adapt state-of-the-art open models to your specific business domain and internal databases. Custom LoRA, QLoRA, and full parameter fine-tuning.
Deploy pipelines directly on your hardware: single-GPU RTX workstations, Jetson edge devices, on-premises A100/H100 clusters, or Nvidia DGX Spark.
Open source moves fast. We help your team pick the right model, quantization, and serving stack for your budget, use case, and latency targets.
This isn't a demo. It's the live AI stack that powers our internal lab - every model self-hosted on a single GB10 Grace-Blackwell DGX Spark, routed through a unified LiteLLM gateway, available at $0 per inference. The same pattern we ship to customers.
Long-form chat · reasoning · multilingual
Coding · agentic tool-calling
60-min meetings · speaker diarization
Default · always-on · 6–9× real-time · English
99 languages
Whisper-medium fine-tune · Uzbek only
Default · always-on · 31 languages · expressive tags
CustomVoice · VoiceDesign · zero-shot Clone
Tashkent dialect · 236 epochs trained locally
Text → image
img2img
Masked inpaint
ESRGAN / SwinIR / 4×-UltraSharp
Detail boost
Suno-style · sync + async render queue
Image → 3D GLB with PBR materials
Text → 3D GLB with PBR materials
Fast voice conversion
Balanced voice conversion
Singing voice conversion · pitch-preserving
Full-duplex S2S · sub-second turn-taking
We hold deep domain expertise optimized across multiple visual and auditory modalities from the open weights ecosystem.
We deploy across every tier of Nvidia silicon - picked to match your latency, memory, privacy, and power budget. Same engineering team, same delivery pattern, four very different form factors.
Desktop AI · 128 GB unified
Run 70B quantized · fine-tune locally · ship in days.
Datacenter scale · multi-tenant
Full FT of 70B+ · high-throughput inference APIs · multi-modal at scale.
Edge / industrial · silent
Voice agents · vision pipelines · disconnected operation.
Workstation · prototyping
Most cost-effective per GPU-hour · standard wall power.
Cloud inference bills scale with usage. Local hardware clusters scale with assets. We build, optimize, and deploy high-performance custom pipelines directly onto edge nodes and local server rooms.
The brand new desktop supercomputer designed for modern LLMs/VLMs. Massive private computing without typical datacenter cooling requirements.
The Nvidia DGX Spark is desktop-class AI hardware with up to 128 GB of unified memory - enough to run quantized 70B LLMs, fine-tune them locally, and serve them over your private network. We're shipping production workloads on Spark today: private RAG, edge fine-tuning, multimodal inference for teams that couldn't justify a datacenter rack.
A quick self-qualifier. Match your workload against the hardware tier - we'll work back from there to a deployment plan.
| Workload | Jetson OrinEdge / IoT | RTX 4090 WSWorkstation | DGX SparkDesktop AI | H100 ClusterDatacenter |
|---|---|---|---|---|
| 7B LLM inference (single user) | ||||
| 70B LLM, AWQ / GPTQ quantized | ||||
| 70B LLM, full FP16 | ||||
| LLM fine-tuning (LoRA, 7B–13B) | ||||
| LLM fine-tuning (full FT, 70B) | ||||
| Text-to-video (LTX-Video / Wan 2.1) | ||||
| Flux.1 image generation | ||||
| Whisper STT (real-time, single stream) | ||||
| TTS voice agent (sub-300ms) | ||||
| High-throughput inference API (1k+ rps) |
We don't believe in generic intelligence. Our specialized pipeline adjusts the weights of the leading open-weight LLMs, VLMs, and voice systems to make them experts in your specific product domain.
Filtering raw databases, structured formatting, and generating synthetically balanced token samples to ensure deep domain coverage.
Adapter-based parameter adjustments locking core model weights, utilizing high-efficiency QLoRA/LoRA to fit compute footprints perfectly.
Comparing fine-tuned performance against base weights on customer benchmark sets, validating zero regression across default intelligence vectors.
Quantizing full parameter merges, baking adapter weightings into the main layers, and packing everything into clean inference runtimes.
Eleven engagements across LLM fine-tuning, edge deployment, video pipelines, vision, voice, 3D, low-resource STT, real-time S2S, and air-gapped infrastructure. Names withheld at client request; methodology and metrics are real.
A legal services portal needed contract-analysis AI inside a private VPC. We fine-tuned Llama-3-8B on 12M lines of contract data and shipped it on their own A100 cluster.
A voice-agent startup needed real-time TTS + STT on Jetson Orin boards with no internet dependency. We compiled Whisper + Kokoro onto the device and hit sub-300ms roundtrip.
A streaming media brand needed cinematic b-roll generated automatically from editorial scripts, matching a specific visual identity. We built an LTX-Video pipeline with a custom brand LoRA and a render queue.
A hospital network needed real-time transcription of physician dictation that never left their network. We fine-tuned Whisper large-v3 on 800 hours of de-identified clinical audio and deployed it on their on-prem GPU cluster.
A multi-brand retailer was waiting weeks for studio photography on every new SKU. We built a Flux.1-based generation pipeline with per-brand style LoRAs, scaled to 12,000 SKUs a day on their existing GPU cluster.
A manufacturer was running a 2018-era CV defect detector that needed retraining for every new product variant. We replaced it with a fine-tuned Qwen-VL deployed on Jetson Orin at every inspection station - generalising across variants without retraining.
A regulated financial institution needed a RAG assistant over millions of policy and compliance documents - and it had to run inside a fully air-gapped environment. We deployed Qwen 3 32B (quantized) on Nvidia DGX Spark with a custom hybrid retrieval stack.
A government digitisation programme needed Uzbek-language speech transcription for citizen-service call recordings. No commercial vendor offered usable accuracy. We fine-tuned Whisper-medium on a curated Uzbek corpus and shipped it as a Dockerised on-prem service.
A furniture retailer wanted AR room-placement for every SKU, but commissioning 3D scans was $80–200 per item. We built a TRELLIS-based image-to-3D pipeline that generates AR-grade GLB meshes from a single product photo in 90 seconds.
A creator economy platform wanted to localise English-language hero creators into 9 languages - without losing the creators' vocal identity. We built a Seed-VC + Whisper + Qwen3-TTS pipeline that keeps the original voice across every language.
A hotel chain wanted an in-room AI concierge that felt as conversational as a human front desk - not the half-second-delayed walkie-talkie experience most voice agents ship. We deployed Nvidia PersonaPlex over WebSocket on local hardware in each property.
We don't offer generic templates or pre-baked packages. We work hand-in-hand with your core technical leads to deliver optimized inference platforms and fine-tuned private models.
Deep dive audit into your current latency, prompt cost footprint, data compliance constraints, and scaling specifications.
Picking the optimal open weights base system and configuring proper bit-level quantization configurations (AWQ/GPTQ/GGUF).
Structuring specific Lora adapter sets or training comprehensive domain-specific weights utilizing our high-speed training queues.
Deploying high-performance containerized API endpoints inside your private cloud VPC, edge node grid, or local server rooms.
We work across the open-source AI stack - from base models on Hugging Face, to inference servers, to the hardware they run on. If a customer brings a model we haven't shipped before, we add it to the list.