Adapt foundation models to your domain — faster and cheaper.
Pre-trained foundation models are powerful generalists, but production applications demand domain expertise. CogniCloud's fine-tuning infrastructure lets you run LoRA, QLoRA, and full fine-tunes on high-performance GPU clusters with automatic fault tolerance, cost-optimised spot scheduling, and experiment tracking built in.
70%
Cost reduction vs on-demand
60 s
From job submit to first GPU
5 min
Max checkpoint interval
∞
Nodes — no hard cluster ceiling
The Challenge
Fine-tuning a 70B-parameter model requires coordinating dozens of GPUs, managing checkpoints across node failures, and tracking hundreds of experiments — all while keeping costs under control. Most teams spend more time fighting infrastructure than improving their model.
How CogniCloud helps
Preemptible Spot instances cut training costs by up to 70%. CogniCloud automatically checkpoints every 5 minutes and resumes on a fresh node with zero manual intervention.
Run parameter-efficient LoRA and QLoRA adapters on a single 8× GPU node, or scale to full fine-tunes across 256 GPUs with FSDP. Same API for both.
Node failures are a fact at scale. CogniCloud detects, replaces, and re-integrates failed workers automatically. Your training job keeps running — you keep sleeping.
Every run is automatically logged with hyperparameters, loss curves, GPU utilisation, and cost-per-token metrics. Compare runs in the dashboard or via the W&B / MLflow integration.
All nodes connect via InfiniBand HDR 200 Gb/s and NVLink 4.0 with 3.35 TB/s bandwidth. NCCL all-reduce saturates the interconnect — gradient sync is never your bottleneck.
Bring your own Dockerfile or choose from optimised base images for PyTorch, JAX, and TensorFlow. Each image ships with tuned CUDA, cuDNN, NCCL, and Flash Attention.
How it works
Describe your training run in a simple YAML config: model, dataset, hardware shape, and budget. CogniCloud picks the optimal spot strategy.
# cognicloud.yaml
job: fine-tune-llama3-70b
model: meta-llama/Llama-3-70B
dataset: s3://my-bucket/dataset
hardware:
gpus: 64
type: high-perf
strategy: spot
budget:
max_cost_usd: 800Push the config with the CLI. CogniCloud provisions nodes, mounts your data, and streams real-time logs, metrics, and cost to the dashboard.
$ cogni run fine-tune-llama3-70b
✓ Provisioning 8 × GPU nodes... 12s
✓ Mounting dataset volume 4s
✓ Starting distributed training
Step 42/10000 loss=1.847 tok/s=148k
Cost so far: $12.40When training converges, publish the LoRA adapter directly to the Inference Gateway with a single command. No weight conversion, no format wrangling.
$ cogni deploy adapter ./checkpoints/step-9800
✓ Adapter uploaded to Neural Cache
✓ Inference endpoint ready
POST https://api.cognicloud.net/v1/chat
Model: meta-llama/Llama-3-70B+my-adapterBuilt on
Orchestrates distributed fine-tuning with fault tolerance and spot scheduling
High-performance GPU clusters with NVLink mesh for gradient communication
Caches base model weights between runs so each experiment starts instantly
Deploy your fine-tuned adapter as an OpenAI-compatible endpoint
Production Inference
Serve any LLM to millions of users at sub-10 ms TTFT.
RAG Pipelines
Ground your LLMs in real knowledge at billion-document scale.
AI for Startups
Move fast, iterate daily — without a dedicated MLOps team.
Enterprise AI
Secure, compliant, and governed AI infrastructure at any scale.
Batch & Offline AI
Process millions of records overnight — at the lowest cost per token.
CogniCloud is in active development. Join the waitlist to get early access and stay updated on our roadmap. No pricing yet — we'll work with each team to find the right fit.
No spam. No pricing pitches. We reach out personally to discuss your use case.