Together AI 对谈 Ollama：开源模型如何服务不同规模的开发者

The non-obvious shift here is that a frontier Chinese model can now be customised inside a US platform, with fine-tuning runs staying in the customer's own Python loop rather than a black-box pipeline. Teams building agents can adapt M3.1 Flash with custom reward signals and environments via SFT or RL. This makes MiniMax a direct alternative to open-weight models fine-tuned on Together, with the model served alongside Western options.

官方账号Together AI@togethercomputeSource

The claim that serving consumes six times the training FLOPs reframes inference as the real cost centre, not model training. Builders running inference workloads can use the full-stack walkthrough to understand why the same open model behaves differently across providers and where compiler and caching gains come from. The segment on SGLang memory management is especially relevant for anyone serving agents with long contexts.

Y Combinator@ycombinatorSource

Day-0 availability on a US inference platform signals Chinese labs continue to route distribution through Western providers to reach global developers. Teams building agents or long-context coding workflows can test a 1M-context model with vision on Together AI immediately, alongside alternatives like DeepSeek and Qwen hosted there.

官方账号Together AI@togethercomputeSource