# Together Compiler: Streamlining AI Model Deployment Efficiency

> The research from Together AI addresses the pressing need for optimization in AI workloads, which are evolving due to the frequent release of new models and diverse architectures. This dynamic landsca...

**Source**: arxiv.org | **Published**: 2026-11-13 | **Type**: research

## Key Facts

- Optimize AI workloads to enhance performance and efficiency in model deployment.
- Implement TKCC to improve inference processes and reduce operational bottlenecks.
- Leverage the Tiered Persistent Engine for faster response times in AI applications.
- Balance measurement throughput and fidelity to maximize overall system performance.
- Continuously adapt AI architectures to keep pace with evolving model releases effectively.

## Summary

**Paper:** [Together Compiler: Continual Compilation for AI Inference](https://arxiv.org/abs/2611.14681)

**Authors:** Reyna Abhyankar, Dylan Lim, Drew Wadsworth, Venkata Hasith Vattikuti, YuTong Julia Zhang, Jung Hwan Heo, Taeksang Kim, Kesavan Ramakrishnan, Evangelene Stanley, Xinyu Fang, Daniel Y. Fu, Simran Arora

\## Executive Summary

The research from Together AI addresses the pressing need for optimization in AI workloads, which are evolving due to the frequent release of new models and diverse architectures. This dynamic landscape poses challenges for performance and efficiency, particularly in the deployment of AI models for inference tasks.

To tackle these challenges, the researchers developed the Together Compiler (TKCC). This innovative solution introduces a continual agent loop specifically designed for optimizing AI inference processes. Traditional systems face a dilemma: they can either focus on high measurement throughput, which often compromises fidelity, or prioritize accurate measurements at the expense of speed. TKCC aims to bridge this gap.

Key features of TKCC include a Tiered Persistent Engine, which separates the overhead involved in launching the engine from the actual computation during the forward pass. This separation allows for quicker responses and more efficient processing. Additionally, TKCC employs a cost model that helps select the optimal parallelism configurations to kickstart the optimization process, streamlining performance improvements.

The research showcases impressive results from extensive testing. TKCC significantly outperforms established hand-written Megakernels, achieving up to 1.7 times better performance on the Llama 1B workload. Moreover, when compared to leading open-source engines on the latest AI models—such as Kimi K3, GLM 5.3, MiniMax M3.1 Flash, and DeepSeek v4.1 Flash—TKCC demonstrated up to 2.6 times higher transaction per second (TPS) on the InferenceX benchmark. Notably, it achieved record TPS numbers at batch size 1, with 1111 TPS on M3.1 Flash and 1205 TPS on DeepSeek v4.1 Flash.

Beyond these benchmarks, TKCC is designed to be versatile, with applications extending beyond just frontier language model inference. In its production environment, TKCC handles trillions of tokens, indicating its scalability and effectiveness in real-world scenarios.

In summary, the development of TKCC marks a significant advancement in the optimization of AI inference processes. By addressing the throughput-fidelity tradeoff and demonstrating superior performance through rigorous benchmarking, TKCC positions itself as a critical tool for companies looking to enhance their AI deployment efficiency.

\## Academic Abstract

Today's AI workloads are rapidly changing: new models are released more often, model architectures are increasingly diverse, and traffic distributions change frequently. The challenge we face at Together AI is continual optimization for these changing workloads. Compiling a deployment into an optimized forward pass is a challenge that is well-suited for agent hill-climbing. Existing systems struggle with continual compilation due to a tradeoff between measurement throughput and fidelity. Many systems use agents to optimize individual kernels from the inference forward pass using offline benchmarks, but timings are low-fidelity because of data-dependent behavior in primitives like mixture-of-experts (MoE). On the other hand, systems use agents to directly optimize end-to-end engines but suffer from slow engine launch times. To address these challenges, we build Together Compiler (TKCC), the first truly continual agent loop designed for AI inference optimization. To address the throughput-fidelity tradeoff, we propose: (1) a Tiered Persistent Engine that decouples engine launch overheads from forward pass computation; (2) a cost model to select the parallelism configurations that seed the optimization process. We also conduct extensive ablations on agent context management and present our insights on deploying TKCC in production. TKCC outperforms highly-optimized hand-written Megakernels on the Llama 1B workhorse workload by up to 1.7x and the best open source engines on the latest frontier models (Kimi K3, GLM 5.3, MiniMax M3.1 Flash, DeepSeek v4.1 Flash) across hardware platforms by up to 2.6x higher TPS on the InferenceX benchmark. TKCC also sets the state-of-the-art performance at batch size 1, with 1111 TPS on M3.1 Flash and 1205 TPS on DeepSeek v4.1 Flash. Finally, TKCC generalizes beyond frontier language model inference. TKCC serves trillions of tokens across our production fleet.

## Frequently Asked Questions

**What business problems does the Together Compiler address?**

The Together Compiler addresses the optimization of AI workloads, particularly the challenges related to performance and efficiency in the deployment of AI models for inference tasks due to the rapidly evolving landscape of new models and diverse architectures.

**Which industries could benefit most from the implementation of the Together Compiler?**

Industries heavily reliant on AI for inference tasks, such as technology, finance, healthcare, and retail, may benefit most from the Together Compiler, as these sectors often require high performance and efficiency in processing AI models.

**What are the practical implementation considerations for businesses looking to adopt the Together Compiler?**

Businesses would need to consider the integration of the Together Compiler into their existing AI systems, the need for adequate infrastructure to support continual optimization, and the potential need to retrain staff to effectively utilize the new technology.

**What resources or expertise might be needed for implementing the Together Compiler?**

Implementing the Together Compiler may require resources such as specialized hardware for AI processing, software development expertise to integrate the compiler with existing AI frameworks, and knowledge in AI model management and optimization.

**What competitive advantages could businesses gain by using the Together Compiler?**

By using the Together Compiler, businesses could gain competitive advantages such as improved efficiency in AI inference, faster response times to market demands, and the ability to deploy more accurate AI models without sacrificing speed, ultimately enhancing their operational effectiveness.

## Links

- [Read on Welcome.AI](https://welcome.ai/content/together-compiler-streamlining-ai-model-deployment-efficiency)
- [Original source](https://arxiv.org/abs/2611.14681)

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Source: Welcome.AI | https://welcome.ai/content/together-compiler-streamlining-ai-model-deployment-efficiency