Model, workload, target hardware and the constraints that cannot change.
RadianVector helps AI teams evaluate and improve how large language models (LLMs) run in production. We do this by testing deployment configurations across precision, KV cache, serving runtime, batching, parallelism, and hardware on real workloads.
You get measured candidate configurations to run - selected across latency, throughput, memory, cost, GPU metrics and other company specific parameters.
From thousands of settings to one decisionMost production LLM configurations were never truly optimized. Someone chose a reasonable setup, tested a few options, confirmed that it worked, and shipped it. That configuration may have been serving production traffic ever since.
The problem is that it has probably never been compared systematically against the thousands of other viable configurations—not because the team doesn’t care, but because doing that properly can take weeks of engineering work
RadianVector turns that weeks-long search into a measured optimization process.
The right combination of serving runtime, precision, batching, parallelism and decoding can reduce time to first token and keep generation responsive under load. Users experience faster answers and smoother interactions—not merely a lower infrastructure bill. It can also preserve responsiveness as traffic grows, helping the product feel dependable rather than merely available.
Well-chosen weight and KV-cache quantization can free memory for longer context windows, larger batches or more concurrent requests. More available context can improve answers when a task depends on conversation history or source material. RadianVector measures those gains against workload quality so additional capacity does not quietly weaken the results.
These opportunities are easy to miss because a deployment can look healthy while users still wait too long, available memory constrains useful context, or quality slips on specific workload slices. Basic uptime and GPU dashboards do not show whether the product is running as effectively as it could.
Model, workload, target hardware and the constraints that cannot change.
RadianCortex identifies a small, diverse shortlist worth a real run.
Quality and performance are measured on the same real workload.
Target policy-compliant shortlist and a saved baseline for future evaluations.
A realistic deployment has eight or more interacting dimensions. Brute force is expensive, slow and mostly wasteful. Engineering, working with insights from RadianCortex, combines compatibility, prior deployment intelligence and deliberate diversity to produce a bounded shortlist; measurement then determines which candidate actually satisfies your policy. RadianCortex is in active development and evolving to improve its search and selection capabilities.
We compare measured candidates across quality, speed, capacity, and cost, then identify the trade-offs that satisfy your constraints.
Quantizers, compilers and serving runtimes each optimize a layer. RadianCortex sits above them: it assembles the relevant candidate set and sends the right few for measurement. Engineering then selects a configuration based on the evidence and your hard constraints.
As new runtime techniques and configuration options become available, RadianVector can run tests relevant to your workload. Evidence from earlier runs helps us focus each new search on the most promising candidates.
INT4 saves a further chunk of memory and holds up on short answers and classification — but long-context tool use falls off sharply. Not worth it unless those requests route somewhere else.
Illustrative — this is the shape of the output, not a measured result.
The first engagement gives us a measured configuration and a repeatable way to compare future candidates on the same workload.
As RadianVector adds evaluation checks, supports new quantization and serving techniques, and improves its search intelligence, we can revisit the deployment and test whether a faster, more capable or more efficient configuration is now available.
See how changes are revalidatedIllustrative workflow — the actual result depends on your model, workload, constraints and hardware.
A new model checkpoint, runtime, driver, hardware target, system prompt or workload can change the quality and performance of a configuration. RadianVector rechecks the affected measures, confirms whether the configuration still clears your requirements and reopens the search when the evidence says the decision should change.
→ The runtime upgrade you're about to take improves throughput, but tool-calling accuracy drops on your workload.
→ The new checkpoint holds quality at a lower precision than the old one did. You can drop a tier and stay above your floor.
Examples of the form, not results we are reporting.
Your engineers understand your product, workload and constraints better than anyone. RadianVector works alongside them to handle the specialized, time-intensive work of searching inference configurations, running controlled tests and measuring the tradeoffs. Your team stays focused on the architecture and product decisions only it can make - while gaining the evidence needed to make each deployment decision faster and with greater confidence.
Your team defines the workload, quality requirements and operational constraints—and makes the final deployment decision. RadianVector handles the iterative testing required to support it.
Gain a focused inference optimization capability without recruiting, onboarding and maintaining a permanent specialist team.
A rigorous configuration search can consume weeks from a senior machine learning engineer. We run the experimental loop so that your engineers can stay focused on product architecture, reliability and the systems only they can build.
Internal efforts often begin by building evaluation harnesses, experiment infrastructure and comparison workflows. RadianVector brings a structured process so the work can move more quickly from requirements to measurement.
RadianVector, informed by RadianCortex, narrows a large configuration space to a bounded set of compatible, diverse and high-value candidates. Your compute and engineering time are spent on experiments that can meaningfully change the decision.
Each engagement produces measured results, a reproducible deployment recipe and a saved baseline. When the stack changes, your team has a clear starting point for targeted revalidation instead of rebuilding the process from the beginning.
RadianVector was built on the insight that improving inference efficiency is an ongoing challenge. As models, runtimes and optimization techniques continue to evolve, teams benefit from a partner dedicated to turning those advances into deployment improvements. We measure the runtime decisions that shape quality, speed, capacity and efficiency, giving teams clearer evidence for how to deploy and continuously improve their inference setup.
We are based alongside the chipmakers and infrastructure teams shaping modern AI inference. New quantization formats, runtimes and accelerators are constantly emerging, so they are in the search space when you want them rather than a year later.
Your model weights, prompts and datasets are yours. We never name a client without written permission. Optional on-prem testing means nothing has to leave your network at all.
Benchmark results are most useful when the methodology, configuration and operating conditions are clear. RadianVector independently tests models and deployed inference configurations, giving engineering teams, customers and partners evidence they can examine and reproduce.
Evaluate the model as it will be deployed—including quantization, runtime, hardware and serving configuration—not simply the original checkpoint under ideal conditions.
Each evaluation documents the test methodology, deployment configuration, measurement conditions and material limitations. The result can serve as a private decision record or the basis for a publishable third-party report.
Co-developed studies, joint research or shared authorship. We are genuinely flexible on structure.
We are flexible and open to discussing various modalities of how best we can help.