Benchmarking inference at scale: coding agents

2026-06-09 · Source: Together AI | The AI Native Cloud - Together.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Expert, medium

Summary

Together Inference Engine demonstrates superior performance for production coding agent workloads, delivering 31% more tokens per second (TPS) than TensorRT-LLM, the next fastest open-source engine, on identical hardware (4x NVIDIA B200 GPUs). It also maintains 2x better time to first token (TTFT) at saturation, achieving 0.71s compared to TensorRT-LLM's 1.1s and SGLang's 5.1s at 2.5M input tokens per minute (TPM). These gains stem from full-stack optimizations, including ThunderMLA, which fuses kernel launches for 20-35% faster decode, custom kernel rewrites, and end-to-end profiling. The benchmark simulates realistic coding sessions with long prompts (45k-200k tokens), high concurrency, and prefill-heavy generation (averaging 450 tokens), emphasizing TTFT sensitivity and concurrent long-context load. Additionally, Kimi K2.6 on Together offers comparable or superior quality to Claude Opus 4.6 on coding benchmarks while being 76% cheaper per request.

Key takeaway

For AI Engineers and MLOps teams deploying coding agents, your focus should shift from single-user benchmarks to high-concurrency, long-context performance. If you are evaluating inference engines, prioritize those demonstrating superior Time To First Token (TTFT) under load, as this directly impacts user experience and system utility. Consider Together Inference Engine with Kimi K2.6 for its proven 2x TTFT advantage and 76% cost savings compared to alternatives like Claude Opus 4.6, offering both performance and economic benefits for your production environment.

Key insights

Production-scale coding agent inference requires full-stack optimization to manage concurrent long-context loads and prioritize Time To First Token (TTFT).

Principles

• Benchmarking must simulate high concurrency.
• Time to first token (TTFT) defines user experience.
• Full-stack optimization improves inference at scale.

Method

The methodology involves stress-testing with long prompts (45k-200k tokens), high concurrency, and prefill-heavy generation on 4x NVIDIA B200 GPUs, measuring TPM, TPS, and p50 TTFT. Optimization uses end-to-end profiling and custom kernel rewrites like ThunderMLA.

In practice

• Implement ThunderMLA for DeepSeek's MLA models.
• Prioritize TTFT in coding agent development.
• Benchmark with concurrent long-context workloads.

Topics

• LLM Inference Benchmarking
• Coding Agents
• Together Inference Engine
• ThunderMLA
• Time To First Token
• GPU Optimization

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Together AI | The AI Native Cloud - Together.ai.