At a glance
| Booth | T9404 |
| Country | SG |
| Website | turbonext.ai |
Company profile
TurboNext.ai is transforming the economics of Generative AI by harnessing heterogeneous compute and cost-effective memory solutions, optimizing large language model (LLM) workloads with model-specific resource allocation and workload-defined hardware. The company's positioning is to turbocharge your existing GPUs to build the world's most efficient LLM system. Efficient LLM inference on heterogeneous hardware with TurboNext.ai: large language model (LLM) inference is crucial for modern AI applications in both research and commercial contexts. While established frameworks like vLLM optimize inference on homogeneous GPU clusters through tensor and pipeline parallelism, strict latency-based SLAs, especially for metrics like time-to-first-token (TTFT) and inter-token latency (ITL), are difficult to satisfy when deploying across heterogeneous hardware. The TurboNext.ai inference software stack leverages resource-aware placement, graph partitioning strategies, and SLA-driven dynamic scheduling for CPUs, GPUs, TPUs, and mixed interconnects. The result is optimal hardware usage, cost efficiency, and scalability, with sustained, predictable user-facing latencies.
Exhibits
Provides Generative AI and LLM workload optimization using heterogeneous compute, cost-effective memory solutions, model-specific resource allocation, and workload-defined hardware.
Capabilities and products
- efficient LLM inference on heterogeneous hardware
- resource-aware placement and graph partitioning strategies
- SLA-driven dynamic scheduling
- optimization for TTFT and ITL latency metrics
- heterogeneous compute and cost-effective memory solutions
- model-specific resource allocation
- turbocharging existing GPUs for LLM systems