Kalavai
Transforms devices into scalable, collaborative AI cloud clusters.. [Freemium]
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What is Kalavai?
Kalavai is an open-source AI infrastructure platform that turns any computing device—desktops, gaming laptops, work computers, and cloud VMs—into a scalable, self-hosted AI cloud. It aggregates GPU resources from multiple machines into a unified LLM pool, enabling teams to deploy and run Large Language Models and Agentic AI workloads without complex DevOps or expensive data-center dependence.
Key features include seamless multi-node LLM deployment, ready-made templates for popular frameworks like llama.cpp, vLLM, and Petals, Ray cluster support for training/fine-tuning/serving, a GUI dashboard for managing pools/devices/GPUs/jobs, CLI tools for pool management, joining tokens for adding workers, and zero-downtime workload redeployment when moving between on-prem and cloud. The platform decouples infrastructure, library code, and model instances to facilitate constant experimentation in both prototyping and production.
Kalavai is designed for AI developers, ML engineering teams, and organizations tired of wasting time on DevOps tasks with hyperscalers. It's ideal for teams with underutilized GPUs at home or office, companies wanting to cut inference costs by up to 80%, and anyone seeking to avoid vendor lock-in while maximizing resource utilization across hybrid cloud and on-prem infrastructure.
The platform addresses the fact that AI developers waste 65% of their time manually procuring VMs, configuring ML/AI tooling, handling failures at scale, and struggling to use resources from multiple providers. Kalavai unifies compute across any infrastructure, supports industry-standard tooling, and empowers teams to unlock collaboration with shared resources.
Kalavai pricing
Pricing model: Freemium
Kalavai is free to use with no caps for both commercial and non-commercial purposes. The open-source client can be installed via pip install kalavai-client at no cost. All core features including LLM pool creation, GPU aggregation, multi-node deployment, GUI dashboard, and CLI tools are included in the free tier. Enterprise plans are available upon contact for tailored solutions. Users can sign up for a waitlist for upcoming enterprise features. Beta testers get early access to the distributed computing platform and access to Kalavai GPUs.
Kalavai pros
- Open source and free to use with no caps for commercial and non-commercial purposes
- Turns any device into a self-hosted AI platform (desktops, laptops, cloud VMs)
- Aggregates GPU resources from multiple machines into unified LLM pools
- Seamless multi-node deployment with zero downtime when redeploying
- Ready-made templates for llama.cpp, vLLM, Petals, and other frameworks
- Ray cluster support for AI training, fine-tuning, and serving
- GUI dashboard for easy pool/device/GPU/job management
- CLI tools for pool creation, token generation, and node management
- No vendor lock-in with hybrid cloud and on-prem support
- Cuts inference costs by up to 80% compared to hyperscalers
- Bring your own workload flexibility
- 3 access modes (admin, user, worker) for granular permission control
- Workloads automatically reassign when nodes leave the pool
- Nodes can rejoin at any point after leaving
- 2x GPU compute improvement through efficient resource utilization
- Easy multi-GPU setup without complex DevOps configuration
- Remote client support allows non-worker computers to access pools
Kalavai cons
- Seed nodes only supported on Linux x86_64 (not Windows or MacOS)
- Worker nodes must be on the same network as the seed node by default
- Requires Docker engine with privilege access for seed and workers
- Requires Python 3.6+ installed on all machines
- Cross-network pool joining requires contacting founders or booking demo
- GPU matchmaking for community resource sharing still in development
- Beta feature flag for enterprise tier (not yet generally available)
- Limited to 2-10 employees company size (early-stage startup)
Frequently asked questions about Kalavai
What is Kalavai?
Kalavai is an open-source AI infrastructure platform that turns any computing device—desktops, gaming laptops, work computers, and cloud VMs—into a scalable, self-hosted AI cloud. It aggregates GPU resources from multiple machines into unified LLM pools, enabling teams to deploy Large Language Models and Agentic AI workloads without complex DevOps or expensive data-center dependence.
How do I install the Kalavai client?
The Kalavai client is a Python package that can be installed with one command: pip install kalavai-client. You need Python 3.6+ and for seed/worker nodes, Docker engine with privilege access installed on Linux, Windows, or MacOS.
How do I create an LLM pool?
After starting the GUI with kalavai gui start, click the circle-plus button on the dashboard, give the pool a name, and select an IP visible to worker machines. Alternatively, use the CLI command: kalavai pool start NAME.
How do I add worker nodes to my pool?
Generate a joining token via the GUI (Devices > circle-plus) or CLI (kalavai pool token --worker), share it with others, and on worker machines paste the token under Access with token and click join. Nodes must be on the same network as the seed node.
What access modes are available for pool members?
Three access modes exist: admin (same access as seed node, can generate tokens and delete nodes), user (can deploy jobs but lacks admin access over nodes), and worker (carries out jobs but cannot deploy their own jobs).
Can I use Kalavai across different networks or locations?
By default, only nodes within the same network as the seed node can join successfully. For cross-network pool joining, you need to contact Kalavai or book a demo with the founders to arrange this capability.
What happens when a node leaves the pool?
Any device can leave the pool at any point by clicking the circle-stop button under Local status or running kalavai pool stop. The workload on that node gets automatically reassigned to other available nodes, and nodes can rejoin at any time following the standard joining procedure.
What frameworks does Kalavai support?
Kalavai provides ready-made templates for popular LLM frameworks including llama.cpp, vLLM, Petals, and more. It also supports running Ray clusters for AI training, fine-tuning, and serving needs.
Is Kalavai free for commercial use?
Yes, Kalavai is free to use with no caps for both commercial and non-commercial purposes. The open-source platform can be used without cost for any use case, including business deployments.
How does Kalavai reduce AI infrastructure costs?
Kalavai cuts inference costs by up to 80% by turning underutilized GPUs into a collaborative AI cloud, eliminating the need for expensive hyperscaler VMs. It maximizes resource utilization across existing hardware and avoids vendor lock-in, allowing teams to use their own computing resources instead of paying premium cloud prices.