Introduction
Together AI is a distributed inference platform designed to run open source models at scale. Unlike traditional cloud solutions, it offers fine-grained control over compute parameters and usage-based pricing. In 2026, optimizing inference is critical for teams looking to reduce costs while maintaining acceptable latencies. This tutorial explores the theoretical foundations of Together AI, its architecture, and strategies for achieving predictable production performance.
Prerequisites
- Basic knowledge of model inference (tokens, batching, quantization)
- Understanding of horizontal scalability concepts
- Familiarity with open source models (Llama, Mistral, Mixtral)
- Notions of latency, throughput, and cost per million tokens
Understanding the Distributed Architecture
Together AI relies on an intelligent routing system that distributes requests across a cluster of heterogeneous GPUs. Each node exposes an OpenAI-compatible interface while locally optimizing memory allocation and kernel scheduling. This architecture allows mixing models of different sizes without reloading weights into memory, thereby reducing cold latency times.
Routing and Batching Strategies
Dynamic routing analyzes prompt size and expected complexity to direct requests to the most suitable instances. Continuous batching allows adding new requests to an ongoing batch, maximizing GPU utilization. These mechanisms rely on scheduling algorithms that anticipate generation duration to minimize overall wait time.
Managing Quantization and Memory
Together AI offers multiple quantization levels (4-bit, 8-bit, FP8) applied dynamically based on load. The key lies in choosing the right precision level for the use case: 4-bit quantization suits classification tasks, while higher precision remains preferable for complex reasoning. The platform automatically handles swapping between VRAM and system RAM when GPU memory is saturated.
Best Practices
- Always measure the generated tokens / input tokens ratio before scaling
- Use dedicated endpoints for predictable workloads instead of shared instances
- Configure timeouts suited to the maximum generation length
- Monitor the KV cache hit rate to detect batching opportunities
- Prefer models already optimized by Together rather than uploading your own checkpoints
Common Mistakes to Avoid
- Neglecting the impact of prompt length on prefill time
- Systematically using temperature 0 without testing other sampling values
- Ignoring effective context limits after quantization
- Forgetting to disable the KV cache on highly variable one-shot requests
Going Further
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