Model Title
Reference to HF
Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.
Size: 50B
Bits: 16b
Max. Context: 10k
Model Title
Reference to HF
Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.
Size: 50B
Bits: 16b
Max. Context: 10k
20x faster boot
Using Instant Provisioning.
10k tokens/s
Throughput for models sized up to 14B parameters.
5x cheaper
Comparing open-source to closed-source models such as GPT-4o.
from cortecs_py.client import Cortecs
from cortecs_py.integrations import DedicatedLLM
cortecs = Cortecs()
with DedicatedLLM(cortecs, model_name='<MODEL_NAME>') as llm:
essay = llm.invoke('Write an essay about LLMs')
print(essay.content)
All Models come with an OpenAI-compatible endpoint, allowing you to use popular tools like LangChain or CrewAI right away.
Dynamic provisioning allows LLMs to be allocated automatically when and where needed, optimizing costs.
GDPR compliant
ISO certified hosting
TLS encryption
On-demand inference is a good choice when applications need reliable, low-latency performance or handle heavy or batch workloads. With dedicated inference, you have exclusive access to a model and its compute resources, so performance remains consistent without competing traffic from other users. This setup is especially effective for tasks that can’t afford delays and need steady resource availability.
Yes we do support custom deployments. Therefore get in touch with our experts.
No, none of your data is stored or used for training.
Instant provisioning allows the access to dedicated language models without the usual setup delays. Instead of dealing with extensive configurations, users gain immediate access to a private LLM endpoint. This setup means that even large models, with many billions of parameters, are instantly available on demand.
Instant provisioning suits batch and scheduled jobs by allowing models to be spun up only when needed, optimizing both resource use and costs. This flexible, on-demand approach minimizes idle time and eliminates operational overhead.
Enterprise
Prefer to run your own fine-tuned model? Connect with our experts for tailored LLM-Inference solutions.
Support
Reach out to customer support for assistance with general inquiries or specific concerns.