Large Language Models

Run and deploy LLMs at scale

Deploy vLLM, SGLang, TensorRT, or your own stack and autoscale on GPUs globally with millisecond latency and usage-based pricing.

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Infrastructure designed for real-world production workloads and reliability at scale.

Why Cerebrium for LLMs?

Features

Why Cerebrium for LLMs?

Instant Startups
Moderns GPUs
Full Inference Control
Global Regions

Built for bursty, unpredictable traffic

Spin up containers globally in 1–2 seconds, even under sudden traffic spikes. Cerebrium scales CPU and GPU workloads on demand without pre-warming or reserved capacity, so you can handle bursts without over-provisioning or idle cost.

NVIDIA H100

Ideal for demanding inference and training tasks

AMD MI300X

High memory bandwidth for large context windows

NVIDIA A100

Optimized for most LLM inference workloads

NVIDIA L4

Efficient choice for low-latency, cost-sensitive tasks

TPU v5e

Google’s scalable TPU for production inference

Cerebrium Flex

Auto-matches hardware to workload needs in real time

Access to the latest GPU hardware

Run workloads on the latest GPU hardware, including B200s, H100s, L40s, and AMD MI300X — without long-term commitments or capacity reservations. Choose the right hardware per workload to balance performance, latency, and cost

Static

Dynamic

Continuous

Your inference stack optimized

Cerebrium is built for high-performance inference at scale. Use static, dynamic, and continuous batching across any inference engine - vLLM, SGLang, TensorRT, and more - to maximize throughput and efficiency.

Data sovereignty at low latency

Deploy workloads in specific regions to meet data residency, compliance, and latency requirements. Cerebrium lets you control where compute runs and where data is processed - without compromising on scale or reliability.

Examples

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