Paiton: The Simplest Way to Supercharge AI Inference

Let’s be honest, we’re not the marketing type.
We’ve never taken a cent of outside investment, never burned cash on ad campaigns, and never hired a sales army.
We just build things that work.

In today’s world, it seems the companies shouting the loudest often get the spotlight, while the ones doing the actual engineering quietly build the future.
We’re the latter.

Still, after a friendly nudge from someone who actually knows marketing, we were told:

So… here we go. 😉

The Smarter Way to Get Faster

In a market flooded with “revolutionary” engines and new runtimes, Paiton takes a simpler, smarter path.
It’s not another inference engine, it’s a performance amplifier for the one you already use.

With Paiton, you don’t need to re-download your models, migrate your stack, or reconfigure your infrastructure.
You just plug it in, and watch your throughput jump.

If you’re using vLLM or SGLang, you’re already compatible.

Plug In, Power Up

Paiton slots right into your existing environment due to it being completely engine agnostic.
No new APIs. No retraining. No learning curve.

Real Performance. Real ROI.

We focused our energy where it matters, deep kernel fusion, custom GEMMs, and optimized inter-GPU communication.
The result: AMD MI300X with Paiton outperforms newer GPUs that cost significantly more, while running on your existing stack.

Performance per Dollar

Meaning: You get 40–50% higher performance at nearly half the cost per token, without changing your stack.

Keep Your Stack. Keep Your Models. Just Go Faster.

Other tools promise performance but force you to rebuild everything.
Paiton simply optimizes what you’re already running.

The Business Math

Every millisecond shaved off inference time translates into real savings.
Here’s what that looks like in practice, based on real-world data from our own Qwen3-30B benchmarks:

Cost per 1M Tokens (USD)

_{Paiton cuts inference cost per million tokens by nearly half compared to H200.}

Result: Faster inference, lower costs, and immediate ROI.

Startup Time Matters Too

Inference isn’t everything, startup speed counts, especially for scaling large models.
For Llama-3.1-405B-Instruct-FP8-KV, Paiton drastically reduced cold-start time by 46% overall.

Why It Matters

There’s a myth that to get better performance, you need a new engine.
We disagree.

Paiton proves smarter software beats newer hardware, and you can have both.
No downloads. No friction. Just better results.

The Bottom Line

If you’re running vLLM, SGLang, or any AMD-based deployment, Paiton is the easiest upgrade you’ll ever make.

• No new engine
• No model downloads
• No workflow disruption
• Instant acceleration and lower cost

Faster inference isn’t just a benchmark, it’s a business advantage.
And Paiton is built to deliver it.

Learn more at https://ai.eliovp.com/paiton

Stay Tuned

Make sure to keep an eye on us, we’re not done raising eyebrows just yet.
We’ve been deep in the trenches pushing FP8 optimization to new limits, both static and dynamic, fine-tuned for Mixture-of-Experts (MoE) models.

The early results? Let’s just say they’re borderline unbelievable,  and all achieved on AMD hardware, without changing your stack or relying on any proprietary APIs.

Stay tuned,  the next drop will redefine what “optimized inference” really means.

Sources / Further Reading

All performance claims in this article are based on our own internal benchmarking (with evaluation models available) on AMD MI300X systems using vLLM/SGLang with Paiton enabled, as documented below:

• Introduction to Paiton by Eliovp (guest post on AMD)
  
• Stop overpaying: Paiton MI300X MoE beats H200/B200 on 1M tokens
  
• MI300X FP8 data-parallel benchmarks (8–64 GPUs): H200 left behind, B200 within reach
  
• Paiton: dramatically faster startup and performance for Llama-3.1-405B