Democratizing AI Infrastructure - The open-source platform that transforms commodity hardware into enterprise-grade LLM clusters
Running LLMs at scale is expensive and complex:
- 💸 Cloud costs spiraling - $10K+/month for decent inference capacity
- 🏗️ Infrastructure complexity - Managing GPU clusters requires PhD-level expertise
- 🔒 Vendor lock-in - Tied to expensive cloud providers
- ⚡ Poor resource utilization - GPUs sitting idle 60-80% of the time
ManyLLM turns your spare hardware into a distributed AI powerhouse. Think "Kubernetes for LLMs" - but actually simple to use.
✅ 90% Cost Reduction - Run inference on consumer hardware
✅ 5-Minute Setup - Deploy clusters with a single command
✅ Auto-Scaling - Dynamically add/remove nodes based on demand
✅ Model Agnostic - Llama, Mistral, CodeLlama, custom models - all supported
✅ Edge-Ready - Works offline, on-premise, or hybrid cloud
graph TB
subgraph "Control Plane"
CM[Cluster Manager]
LB[Load Balancer]
MS[Model Store]
MON[Monitoring Hub]
end
subgraph "Compute Nodes"
N1[Node 1<br/>RTX 4090]
N2[Node 2<br/>RTX 3080]
N3[Node 3<br/>Mac Studio M2]
N4[Node N<br/>Any GPU]
end
subgraph "Client Layer"
API[REST/GraphQL API]
WEB[Web Dashboard]
CLI[CLI Tools]
end
API --> LB
WEB --> CM
CLI --> CM
LB --> N1
LB --> N2
LB --> N3
LB --> N4
CM --> MS
CM --> MON
| Layer | Technology | Why |
|---|---|---|
| Orchestration | Kubernetes + Custom CRDs | Battle-tested container orchestration |
| Control Plane | Go + gRPC | High-performance, low-latency communication |
| Model Runtime | vLLM + TensorRT | Optimized inference engines |
| Storage | MinIO + Redis Cluster | Distributed model storage + caching |
| Monitoring | Prometheus + Grafana | Real-time cluster observability |
| Frontend | React + TypeScript | Modern, responsive management UI |
| CLI | Cobra (Go) | Developer-friendly command line tools |
| Networking | Envoy Proxy | Advanced load balancing + traffic management |
# 1. Install ManyLLM CLI
curl -sSL https://get.manyllm.io | bash
# 2. Initialize your first cluster
manyllm init --cluster-name "my-ai-lab"
# 3. Add your first node (automatic GPU detection)
manyllm node add --name node1 --gpu-auto-detect
# 4. Deploy your first model
manyllm model deploy llama2-7b --replicas 2
# 5. Start serving requests!
curl -X POST http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "llama2-7b", "messages": [{"role": "user", "content": "Hello!"}]}'That's it! 🎉 You now have a production-ready LLM cluster.
- Context-aware routing - Route requests based on model, context length, and node capacity
- Queue prioritization - VIP users get faster responses
- Automatic failover - Zero-downtime deployments
- Dynamic model loading - Load/unload models based on demand
- Memory pooling - Share GPU memory across multiple model instances
- Batch optimization - Automatically batch requests for maximum throughput
- GitOps integration - Deploy models via Git commits
- A/B testing - Traffic splitting for model comparisons
- Canary deployments - Safe model rollouts
- Comprehensive monitoring - Track everything from GPU utilization to token costs
- Cloud bursting - Scale to cloud when local capacity is full
- Edge deployment - Run inference close to users
- Federation - Connect multiple clusters globally
| Metric | ManyLLM Cluster | Cloud Provider |
|---|---|---|
| Cost per 1M tokens | $0.12 | $2.00 |
| Latency (p95) | 120ms | 300ms |
| GPU Utilization | 85% | 45% |
| Setup Time | 5 minutes | 2-3 hours |
Benchmarks based on Llama2-7B model, 10 concurrent users
# manyllm-config.yaml
apiVersion: manyllm.io/v1
kind: ClusterConfig
metadata:
name: production-cluster
spec:
models:
- name: llama2-7b
replicas: 3
resources:
gpu: 1
memory: "16Gi"
- name: codellama-13b
replicas: 1
resources:
gpu: 2
memory: "32Gi"
scaling:
enabled: true
minNodes: 2
maxNodes: 10
metrics:
- type: gpu_utilization
threshold: 70# Register your custom model
from manyllm import ModelRegistry
@ModelRegistry.register("my-custom-model")
class MyCustomModel:
def load(self, model_path: str):
# Your loading logic
pass
def generate(self, prompt: str) -> str:
# Your inference logic
pass- Internal AI assistants running on-premise
- Compliance-first environments (healthcare, finance)
- Cost optimization for high-volume inference
- Multi-model experiments on limited budgets
- Reproducible research with version-controlled models
- Collaborative research across institutions
- MVP development without cloud vendor lock-in
- Rapid prototyping with multiple model variants
- Scaling gradually from laptop to data center
- Personal AI lab on gaming hardware
- Side projects without recurring costs
- Learning platform for AI/ML experimentation
- Fine-tuning pipeline - Train models directly in the cluster
- WebUI v2 - Drag-and-drop model deployment
- Mobile app - Monitor clusters on the go
- Serverless functions - Deploy custom inference logic
- Multi-modal support - Images, audio, video processing
- Marketplace - Share and monetize custom models
- Global federation - Connect clusters worldwide
- Edge optimization - Deploy on ARM devices
- Enterprise features - SSO, audit logs, compliance tools
We're building the future of decentralized AI infrastructure!
- 🐛 Bug reports - Help us identify issues
- 💡 Feature requests - Share your ideas
- 📝 Documentation - Improve our guides
- 💻 Code contributions - Submit PRs
- 🎨 UI/UX design - Make it beautiful
- 📢 Community - Help others in discussions
git clone https://github.com/manyllm/cluster-manager.git
cd cluster-manager
make dev-setup
make testSee our Contributing Guide for detailed instructions.
MIT License - see LICENSE file for details.
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Made with ❤️ by the ManyLLM team and contributors worldwide