MORE FROM AMD GPUS. PROVEN ON REAL MODELS.
Paiton finds what slows your model down, replaces the generic runtime path,
and turns AMD hardware into faster production inference without retraining.
MEASURED ON REAL GENERATION AND INFERENCE PATHS.
The gap is rarely the model. It is usually the runtime.
Paiton tunes the exact path your workload uses, then proves the gain with benchmarks.
Video diffusion: MI355X vs B200
Paiton-Diffusers removed runtime waste from the generation path and let the hardware show up.
Read Wan2.2 BenchmarkMoE inference: MI300X vs H200/B200
Profile the bottleneck, write the AMD path that should have existed, and measure the result.
Read MoE BenchmarkYou do not need a new model to improve unit economics. You need the runtime, kernels, and deployment path tuned around the workload you already run.
LLMS, VIDEO DIFFUSION, MOE, AND CUSTOM STACKS.
Whether you run LLMs, video diffusion, MoE, or custom architectures,
Paiton starts where money is lost: latency, throughput, memory pressure, and cost per token.
More tokens, less waiting
Find the bottleneck that users and invoices feel. Llama-3.1-405B work showed a 65% boost path.
No retraining required
Keep the model and improve the path around it with AMD-aware operators and custom .so files.
Runtime work that pays back
Paiton-Diffusers for Wan2.2 video generation and vLLM support where language inference needs speed.
Language Models
Llama, Qwen, Deepseek, Gemma, Mistral, and CodeLlama with vLLM support, FP8 precision, and custom kernels when the gain is worth it.
Diffusion & Video
Wan2.2, Flux, Stable Diffusion, SDXL, ControlNet, and text-to-video workloads where scheduler, memory, and kernel choices move the number.
Advanced Models
MoE, MLA, multimodal systems, proprietary architectures, expert kernels, novel attention, and custom inference stacks.
MEASURE. TUNE. PROVE.
Bring the workload, the target GPU, and the performance goal.
We turn that into a focused path from measurement to production-ready gain.
Run the real workload and identify where latency, memory pressure, or cost per token is leaking value.
Build the AMD-specific path with custom .so files, FP8 precision, kernel fusion, and the same model weights.
Deploy the optimized path on AMD Instinct infrastructure and compare the result against the baseline.
FP8 can reduce memory pressure while preserving accuracy. Tensor parallelism spreads large models like Llama-3.1-405B across multiple AMD GPUs. Custom kernels target the bottlenecks generic runtimes leave behind.
MI355X
288GB HBM3E
MI325X / MI300X / MI300A
256GB HBM3E, 192GB HBM3, 128GB HBM3 APU
MI250X / MI250 / MI210
Datacenter AMD GPU support
MI100
First compute-optimized generation
RX 7900 XTX / RX 7900 XT / RX 6800 XT / RX 6900 XT
Consumer GPUs with community ROCm drivers
Best performance is on CDNA datacenter GPUs. RDNA support remains beta for teams testing outside the datacenter stack.
17.6% faster
Text-to-video diffusion speedup with Paiton-Diffusers.
Read Benchmark405B model
Faster startup and inference for a frontier-scale open model.
Read Case Study$/1M tokens
MI300X plus Paiton runtime on long-context MoE economics.
Read BenchmarkCost/performance
A practical cost-efficiency read on AMD MI300X versus NVIDIA H200.
Read Analysis
Share the model, target GPU, and production goal.
Paiton turns that into a benchmark-led optimization plan.
