In a significant development for the artificial intelligence sector, Together AI has unveiled innovative optimization techniques that promise to reduce AI inference costs by up to five times while also enhancing response times. This breakthrough comes as the demand for efficient AI solutions continues to grow among enterprise clients.
According to Together AI, the common misconception that larger AI models are the primary reason for slow response times is incorrect. Their analysis indicates that issues such as memory stalls, inefficient kernel scheduling, and idle GPUs awaiting data transfers are the real bottlenecks. Their extensive benchmarks across various model families, including Llama, Qwen, Mistral, and DeepSeek, highlight that addressing these pipeline challenges—not merely upgrading hardware—yields the most substantial improvements.
One key strategy identified is quantization, which involves reducing model precision. Together AI reports that transitioning from FP16 to FP8 or FP4 can result in throughput increases of 20-40% without any noticeable quality loss when implemented correctly. A smaller memory footprint allows for larger batch sizes, which translates to processing more tokens per dollar spent. Furthermore, knowledge distillation techniques yield even greater cost efficiencies, with DeepSeek-R1″s distilled variants achieving costs 2-5 times lower while maintaining similar quality levels.
Geographical considerations also play a crucial role in reducing latency. Deploying lightweight proxies close to inference clusters can decrease the time-to-first-token by 50-100 milliseconds, eliminating unnecessary network round trips. This approach aligns with the broader trend in the industry towards edge AI deployment, which enhances both speed and data privacy.
Teams utilizing multi-token prediction (MTP) and speculative decoding are experiencing notable improvements in decoding speeds. MTP allows for the simultaneous prediction of multiple tokens, while speculative decoding employs a draft model to expedite generation for predictable workloads. When these techniques are fine-tuned, Together AI claims a 20-50% increase in decoding speed.
While hardware selection remains important, particularly with NVIDIA”s Blackwell GPUs and Grace Blackwell (GB200) systems providing significant throughput benefits, the real challenge lies in effectively leveraging these advancements. Strategies such as tensor parallelism and expert parallelism are essential to fully exploit the potential of high-performance hardware.
For AI developers and businesses, the path forward is clear: begin by measuring baseline metrics such as time-to-first-token, decoding rates, and GPU utilization. Subsequently, address identified bottlenecks by deploying regional proxies, enabling adaptive batching, and utilizing speculative decoding techniques. Companies like Cursor and Decagon are already implementing these strategies, achieving response times below 500 milliseconds without proportionately increasing their GPU expenditures. While these methods are straightforward, they have yet to be fully adopted across the industry.
