Solution · Accelerate
Frontier performance — often better — from optimized models on the fastest silicon, run as a service.
A tuned open model on Cerebras-, Groq- or Blackwell-class inference beats a rented frontier API on the tasks that matter to you: it answers in a fraction of the time, it never leaves your control, and it costs what hardware costs. We build the pipeline around it — video, retrieval, alerting, voice, control — and operate it with an SLA.
Why speed changes the product
At a thousand tokens a second, agents, video and voice stop being batch jobs.
| Tier | Throughput | On-premise / dedicated | Where we use it |
|---|---|---|---|
| Cerebras CS-3 / CS-4 (WSE-3 Turbo, rack-scale CS-4 shipped Aug 19, 2026) | Llama 3.3 70B 1,800+ tok/s; gpt-oss-120B ~3,000; Qwen3-Coder-480B ~2,000 | Yes — systems purchasable; “Cerebras for Nations” sovereign track | Agent loops, real-time voice, interactive video Q&A, code generation at scale |
| Groq LPU | Llama 3.3 70B ~250–315 tok/s | Yes — GroqRack; Bell Canada’s AI fabric runs on it | Canadian in-country dedicated capacity |
| SambaNova SN40L / SambaStack | Llama 3.1 70B 461 tok/s; DeepSeek-R1 671B 198 tok/s | Yes — turnkey in ~90 days; US DOE labs, OVHcloud, stc | Largest models on-premise with a small footprint |
| NVIDIA GB300 NVL72 | ~6,005 tok/s per GPU on DeepSeek-R1; 1.1M tok/s aggregate per rack on Llama-2 70B (MLPerf v5.1) | Yes — every OEM; the general-purpose sovereign standard | Broadest model catalog, VSS and Metropolis video pipelines, digital twins, air gaps |
Reference points: OpenAI’s GPT-5.3-Codex-Spark, its first production model not on NVIDIA GPUs, serves at 1,000+ tokens per second on Cerebras. A Cerebras and Hugging Face voice pipeline (Parakeet speech recognition, Gemma 4, Qwen3 text-to-speech) runs under 100 milliseconds end to end in more than 9,000 Reachy Mini robots in production. Ultra-fast inference is not exclusive to wafer-scale silicon: the numbers above show NVIDIA’s rack posting the highest aggregate throughput of any platform.
Pipelines we operate
Three stacks, one tier of speed.
Video search and analysis. The NVIDIA Video Search and Summarization pattern — a vision-language model, an LLM and retrieval over video — summarizes an hour of footage in under a minute and answers questions over live streams and archives with alerts. We deploy it over site cameras, factory floors and security archives on your GPUs: search by event, summarize a shift, alert on a safety condition, retain nothing off-site.
Identification and security operations. In the NVIDIA Morpheus pattern — proven at Best Buy on phishing classification at 96 percent accuracy with under 20 percent false positives — models triage logs, tickets and mail at line rate. For identification we match badges, vehicles and documents against your own records under your access policy, inside your perimeter.
Robotics and real-time voice. A sub-100-millisecond speech loop is the proven reference; the same tier drives planning and instruction for robots validated first in an Omniverse and Isaac digital twin, with Jetson Thor and the Cosmos edge models on the machine. See the ultra-fast inference blueprint and the digital twins and multimodal agents blueprint.
Honest labelling. Video search and security operations on Cerebras-class hardware specifically are architectures we propose and benchmark on your data, not published vendor case studies. The voice pipeline, the VSS throughput and the Morpheus results are published references. Every proposal says which is which.
As a service
What “as a service” includes.
Optimize
The model for the job
Open weights selected, quantized, fine-tuned where it pays, and benchmarked on your data against the frontier API you would otherwise rent — before hardware is committed.
Build
The pipeline around it
Ingestion, retrieval, guardrails, alerting, voice or control loops, and the gateway with budgets and audit. NVIDIA AI Blueprints and NIM under an NVIDIA AI Enterprise subscription where they fit; self-hosted under its terms.
Run
On your terms, with an SLA
Hardware you own, or dedicated capacity in your jurisdiction. Monitoring, evaluations, model upgrades and the latency budget held — see hosting & maintenance.
Questions we get
Frequently asked questions
What does “optimized models and pipelines as a service” mean?
We take an open-weight model, tune and quantize it for one job, put it on the fastest inference tier that job justifies, wrap it in the pipeline around it — video ingestion, retrieval, alerting, a voice loop, a control loop — and run it for you as a service with an SLA, on hardware you own or on dedicated capacity in your jurisdiction. The output is a product that behaves like a frontier API on your task, often faster, and never sends data anywhere.
How fast is ultra-fast inference, really?
Cerebras serves 70B-class models at 1,800 tokens per second and larger MoE models at 2,000 to 3,000, against roughly 250 to 460 tokens per second on Groq and SambaNova, with NVIDIA’s GB300 NVL72 rack posting about 6,000 tokens per second per GPU on DeepSeek-R1 in MLPerf. Cerebras shipped its rack-scale CS-4 on August 19, 2026, and OpenAI’s GPT-5.3-Codex-Spark runs on Cerebras at over 1,000 tokens per second. At that speed an agent loop, a video pipeline or a voice exchange stops feeling like a batch job.
Which of these use cases are proven and which are proposals?
Proven references: a sub-100-millisecond voice pipeline from Cerebras and Hugging Face running in more than 9,000 Reachy Mini robots; NVIDIA’s Video Search and Summarization blueprint summarizing an hour of video in under a minute; NVIDIA Morpheus at Best Buy classifying phishing at 96 percent accuracy with under 20 percent false positives; and sovereign programs from Cerebras, Groq (Bell Canada) and SambaNova. Video search and security operations on Cerebras-class hardware specifically are architectures we propose and benchmark on your data, not published vendor case studies. We say which is which in every proposal.
Do we have to buy a Cerebras system?
No. Cerebras, Groq and SambaNova all sell dedicated and on-premise capacity, and NVIDIA Blackwell racks are available through every major OEM. Most clients start on dedicated endpoints or a GPU node in their own facility, and we move the latency-critical pipeline to wafer-scale or LPU hardware only when the workload proves it needs it. Cerebras’s public rate card is narrow — gpt-oss-120B at $0.35 in and $0.75 out per million tokens — with the broader catalog on custom-priced dedicated endpoints.
Is this still sovereign?
Yes, by construction: open weights, your data, hardware you own or dedicated capacity in your jurisdiction, and a gateway and audit log you control. All three fast-inference vendors run sovereign programs for governments for exactly this reason. Where an air gap is required, the NVIDIA tier and on-premise Cerebras or SambaStack systems run disconnected.
Go deeper
Blueprint: ultra-fast inference for video, security and robotics
The three pipelines, the vendor comparison and what is proven versus proposed.
Blueprint: digital twins and multimodal agents
Self-hosting NVIDIA AI Blueprints — Mega, VSS, PDF extraction, AI-Q, GR00T.
Frontier & routing
When the fast tier sits beside frontier endpoints behind one gateway.
Air-gapped AI
The disconnected variant for defense and controlled data.
Construction & development
Site cameras, safety alerts and the second brain.
The model bench
The open weights we optimize and serve.
Benchmark your task against the frontier API. Then own the faster answer.
The two-week sovereignty assessment picks the model, sizes the tier and prices the pipeline as a service — with a benchmark on your own data before anything is bought.
Book a sovereignty assessment