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On-Demand Language Models, Instantly Ready to Handle Massive Workloads.
Model Title
Reference to HF
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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
No Rate Limits, No Delays
Launch Your Own Dedicated Model Instantly – Built for Big Data.
4sec.
Instant Provisioning for the world's fastest deployments.
45k tokens/s
Throughput for models sized up to 14B parameters.
50% lower costs
Compared to conventional cloud providers.
Instantly Ready
Launch effortlessly and leave infrastructure to us.
...
client = Cortecs()
#Choose model that has a big context length
model_name = "cortecs/DeepSeek-R1-Distill-Qwen-32B-FP8-Dynamic"
book = requests.get("https://www.gutenberg.org/cache/epub/5200/pg5200.txt").text
question = "Based on the provided text, who is Gregor Samsa?"
tokenizer = AutoTokenizer.from_pretrained(model_name)
len_tokenized_book = len(tokenizer.encode(book)) # ~32k
#Add 1k tokens for question and output
cag_context_length = len_tokenized_book + 1000
instance = client.ensure_instance(model_name,
context_length=cag_context_length)
llm = ChatOpenAI(model_name=model_name, base_url=instance.base_url)
llm.invoke(book + f"\n{question}")Drop-in replacement.
All models include an OpenAI-compatible endpoint, so you can seamlessly use the OpenAI clients you're already familiar with.
Dynamic provisioning.
Use an API to start and stop your models, with resources seamlessly allocated in the background.
Cache augmented generation.
CAG allows dynamic adjustment of context length, balancing efficiency and relevance by reusing stored outputs as needed.
European Standards.
Designed for maximum security and guaranteed availability.
GDPR compliant
TLS encryption
Fail-Safety
Top Security
Frequently Asked Questions
What's an on-demand deployment?
On-demand or dedicated deployments are ideal for applications requiring reliable, low-latency performance or handling heavy workloads. They provide exclusive access to a model and its compute resources, so performance remains consistent without competing traffic from other users.
This approach is particularly effective for high-demand tasks like batch processing or cache-augmented retrieval (CAG).
Can I use my own model?
We support any language model on Hugging Face. Please post your request to our discord channel.
Is my data stored or used for training?
No, none of your data is stored or used for training.
What's Instant Provisioning?
Instant provisioning allows the access to dedicated language models without the usual setup delays. By utilizing a warm start, users can instantly access their dedicated endpoint, even for large models with billions of parameters. This on-demand availability ensures rapid performance without waiting for initialization.
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