At a glance
| Booth | T9404 |
| Country | SG |
| Website | turbonext.ai |
Company profile
TurboNext.ai focuses on the economics of Generative AI by combining heterogeneous compute with cost-effective memory solutions rather than presenting compute and memory as separate optimization areas. Its large language model (LLM) approach optimizes workloads through model-specific resource allocation, so resource assignment is described in relation to the particular model being run. The same LLM optimization approach uses workload-defined hardware, linking the hardware configuration to the workload definition as part of the company's system concept. TurboNext.ai also presents its technology as a way to turbocharge existing GPUs, placing existing GPU resources within its approach to building efficient LLM systems. The company's product message is centered on building what it describes as the world's most efficient LLM system through this combination of compute, memory, resource allocation, and workload-defined hardware.
Exhibits
The flagship TB1 Inference System is the named inference product in the lineup and is presented as the system that brings together the company's memory and software-stack features for LLM inference. TB1 includes large on-board memory, making on-board memory capacity one of the specifically identified components of the flagship inference system. The TB1 Inference System is specified with 100% KV-cache hit rates, a stated cache-hit characteristic that is part of the flagship system description. TB1 also uses a parallelized software stack, so the flagship system description combines a software-stack characteristic with its on-board memory and KV-cache features. TurboNext.ai's broader LLM workload strategy uses heterogeneous compute and cost-effective memory solutions, providing the compute-and-memory context in which its inference-system offering is presented. For workload optimization, model-specific resource allocation is named alongside workload-defined hardware, making both resource allocation and hardware definition explicit elements of the LLM system approach. The company describes the approach as turbocharging existing GPUs, so its LLM system positioning is based on enhancing the use of GPUs that are already available rather than describing a replacement-only GPU strategy. The product positioning joins existing GPUs with heterogeneous compute, cost-effective memory, model-specific resource allocation, and workload-defined hardware under a single LLM optimization direction. Within that direction, TB1 contributes three expressly listed system characteristics: large on-board memory, 100% KV-cache hit rates, and a parallelized software stack. Taken as a complete offering, TurboNext.ai presents LLM inference optimization as a combination of its resource-allocation and hardware-definition method with the TB1 Inference System's named memory, cache-hit, and parallelized-software characteristics.
Capabilities and products
- LLM optimization with heterogeneous compute and cost-effective memory
- model-specific resource allocation for LLM workloads
- workload-defined hardware for LLMs
- LLM acceleration for existing GPUs
- inference architecture with 100% KV-cache hit rates
- parallelized LLM software stack