Hardware-focused data centers

Your hardware is efficient.
We help with the software.

You have made your hardware offering efficient. We measure what your machines deliver on real AI workloads, and help your clients get the most out of them.

  1. The biggest gap

    Compete with inference platforms, without hiring an inference team.

    Inference platforms and model API providers use the same GPUs as you, but earn larger margins and have more customers. They win them with the software they run on that hardware: tuned engines, settings and measured results.

    • Most hardware-focused data centers don’t have that software, or a GPU engineering team to build it.
    • Hiring experienced GPU and software engineers is extremely expensive.
    • RadianVector brings that expertise, with a large set of GPU optimization and benchmarking tools we have built.
    • We tune and measure inference on your hardware, so you sell tuned, measured performance, not just capacity.
  2. Help your clients get the most out of your machinesWe find the serving engine, settings and quantization that give each client the answers, speed and cost they need on your hardware.
  3. A check of your own benchmark numbersWe rerun your published benchmarks and confirm or correct the numbers, so your clients can trust them.
  4. Independent benchmarks on your serversThroughput, response time and GPU energy per token for the models your clients run, measured by a third party.
  5. Lower energy per tokenWe measure output per watt on your GPUs, and tune the serving setup to make it more efficient and reduce energy per output token.
Published engineering work

Inference we have measured,
on real GPUs.

Results from our open-model serving tests, with the versions we tested.

Throughput

SGLang vs vLLM: one setup, two speeds

When one request arrived a few milliseconds early, SGLang 0.5.20 made 3 requests wait, and total throughput fell by 40%. 637 runs, and the settings that close the gap.

Read the deep dive →
Benchmarks

Qwen3.8-27B on the RTX 4090 GPU family

Quantization, KV cache, CUDA graphs, concurrency and speculative decoding, with accuracy measured alongside speed.

Inspect the benchmarks →
Answer correctness

Found and fixed a vLLM bug

From vLLM 0.28 through 0.31, MTP speculative decoding could repeat or skip tokens on hybrid models such as Qwen3.5. We traced the cause and tested a fix.

Read the investigation →

See all published research →

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Sell more than capacity.
Let’s measure your machines.