Clarifai Reasoning Engine Achieves 414 Tokens Per Second on Kimi K2.5

2026-03-16 · Source: Clarifai Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, quick

Summary

Clarifai's Reasoning Engine has achieved a throughput of 414 tokens per second (TPS) on the Kimi K2.5 model, making it one of the first providers to surpass 400 TPS for a trillion-parameter reasoning model. This performance, validated by Artificial Analysis and running on Nvidia B200 GPU infrastructure, positions Clarifai among the top inference providers for frontier reasoning models. Kimi K2.5, a 1-trillion-parameter model with a 384-expert Mixture-of-Experts architecture, activates 32 billion parameters per request and exhibits strong benchmark performance, including 50.2% HLE with tools. Clarifai's optimization stack includes custom CUDA kernels, speculative decoding, and adaptive optimization to enhance throughput and reduce time to first answer token, which is 6 seconds for Kimi K2.5.

Key takeaway

For CTOs and VPs of Engineering evaluating inference solutions for complex reasoning models, Clarifai's 414 TPS performance on Kimi K2.5 demonstrates that production-grade speed is achievable. Your teams can leverage these optimized engines to deploy agentic systems and multimodal reasoning tasks at scale, ensuring efficient end-to-end response times for critical applications. Consider integrating Kimi K2.5 on the Clarifai Platform for high-performance reasoning workloads.

Key insights

Optimized inference engines can achieve high throughput for large reasoning models using specialized techniques and hardware.

Principles

• Low-level GPU optimization reduces memory stalls.
• Speculative decoding minimizes wasted computation.
• Adaptive optimization improves performance over time.

Method

Clarifai optimizes large reasoning model throughput using custom CUDA kernels for GPU efficiency, speculative decoding to predict token paths, and adaptive optimization for dynamic workload adjustment.

In practice

• Deploy Kimi K2.5 for agentic workflows.
• Utilize custom CUDA kernels for inference.
• Implement speculative decoding for reasoning tasks.

Topics

• Kimi K2.5
• Speculative Decoding
• CUDA Kernels
• Reasoning Models
• GPU Inference

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Clarifai Blog.