Next-generation compute infrastructure
We provide a fully optimized compute environment, from bare-metal hardware to container orchestration, tuned layer by layer for demanding workloads.
Serverless scheduling
Scale instances with live traffic, reduce idle capacity, and keep inference endpoints responsive under changing demand.
Low-latency inference acceleration
Optimized TensorRT and vLLM runtime paths improve throughput and response time compared with generic serving stacks.
1// High-throughput inference with vLLM2from vllm import LLM, SamplingParams34prompts = ["Explain quantum computing in 100 words."]5sampling_params = SamplingParams(temperature=0.8, top_p=0.95)67llm = LLM(model="llama-3-70b-instruct", tensor_parallel_size=4)8outputs = llm.generate(prompts, sampling_params)Serverless scheduling
Scale instances with live traffic, reduce idle capacity, and keep inference endpoints responsive under changing demand.
Low-latency inference acceleration
Optimized TensorRT and vLLM runtime paths improve throughput and response time compared with generic serving stacks.
Global active-active deployment
Regional compute pools and redundancy help inference services stay stable during high-concurrency traffic spikes.
Recommended compute profiles
For this workload, we selected GPU profiles that balance performance, efficiency, and delivery readiness.
