# SOLLOL Roadmap

**Vision:** Make distributed AI compute accessible to independent engineers — bridging the gap between one-machine demos and real cluster orchestration without cloud dependency.

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## Current Status: v0.9.x (Alpha)

**Production-Stable Features:**
- ✅ Intelligent task distribution across Ollama nodes
- ✅ Auto-discovery and failover
- ✅ Real-time observability dashboard
- ✅ GPU/CPU resource-aware routing
- ✅ Priority-based scheduling
- ✅ Response caching and HTTP/2 support
- ✅ Batch processing API

**Experimental Features:**
- 🔬 Distributed inference via llama.cpp RPC (proof-of-concept)

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## v1.0 — Production Hardening (Q1 2025)

**Goal:** Production-ready for teams running multi-node AI infrastructure

### Core Stability
- [ ] **Performance validation** - Independent multi-node benchmarks
- [ ] **Error handling** - Comprehensive retry logic and circuit breakers
- [ ] **Production deployment** - Docker Compose + Kubernetes manifests
- [ ] **Security hardening** - TLS/SSL, API authentication, rate limiting
- [ ] **Monitoring** - Grafana dashboards, alerting rules

### Developer Experience
- [ ] **Python SDK improvements** - Better error messages, type stubs
- [ ] **CLI enhancements** - Interactive setup wizard, node management
- [ ] **Documentation** - Video tutorials, deployment playbooks
- [ ] **Examples** - LangChain, CrewAI, AutoGPT integrations

### API Stability
- [ ] **API versioning** - Backwards compatibility guarantees
- [ ] **Schema validation** - OpenAPI 3.1 specs
- [ ] **Migration guide** - Upgrade path from v0.9.x

**Release Target:** March 2025

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## v1.x — Feature Expansion (Q2-Q3 2025)

### Enhanced Intelligence
- [ ] **ML-based routing** - Learn optimal node selection from historical patterns
- [ ] **Cost-aware routing** - Consider energy/cloud costs in decisions
- [ ] **Predictive scaling** - Auto-scale based on queue depth and patterns
- [ ] **A/B testing framework** - Compare routing strategies in production

### Multi-Cloud & Hybrid
- [ ] **Cloud provider integrations** - AWS Bedrock, Azure OpenAI fallback
- [ ] **Geographic routing** - Latency-aware multi-region support
- [ ] **Hybrid deployments** - Mix local + cloud seamlessly

### Developer Tools
- [ ] **VSCode extension** - Monitor cluster from IDE
- [ ] **Jupyter integration** - Notebook-native cluster management
- [ ] **Webhooks** - Event notifications for failures, scaling events

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## v2.0 — Advanced Distributed Compute (Q4 2025+)

### True Model Weight Sharding
**Current limitation:** llama.cpp coordinator requires full model in RAM

**v2.0 Goal:** Run 70B+ models with NO single node needing full model

**Research Track (Funding/Partnership Required):**
- [ ] **GGUF tensor-level distribution** - Split model weights across nodes
- [ ] **Ray-based pipeline parallelism** - Activation passing via object store
- [ ] **Quantization-aware sharding** - Smart layer distribution
- [ ] **Production validation** - 70B-405B models on consumer hardware

**Impact:** Enables sovereign AI deployment at scale without cloud dependency

### Advanced Features
- [ ] **Model fine-tuning pipeline** - Distributed training workflows
- [ ] **Multi-tenancy** - Isolated workspaces with quotas
- [ ] **GraphQL API** - Flexible query interface
- [ ] **WebSockets** - Real-time streaming and bidirectional communication

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## Research Tracks

### 1. Distributed Inference Optimization (High Priority)
**Status:** Experimental (5x slower than local, manual setup)

**Path to Production:**
- Reduce startup time: 2-5min → <30s
- Improve throughput: 5x slower → 2x slower
- Automated version management and binary compatibility
- Comprehensive testing across model sizes (13B-70B+)

**Blocker:** Requires dedicated cluster access and optimization time

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### 2. Zero-Config Deployment
**Goal:** `pip install sollol && sollol start` → instant cluster

**Requirements:**
- Auto-install Ollama on discovered nodes
- Intelligent model distribution (which models go where)
- Self-healing configuration
- One-click cloud deployment (AWS/GCP/Azure)

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### 3. Agent Marketplace
**Vision:** Share and discover SOLLOL-optimized agent configurations

**Features:**
- Pre-configured routing strategies for common use cases
- Tested multi-agent orchestrations (research, coding, analysis)
- Community ratings and benchmarks
- One-click deployment

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## Community Priorities

Vote on features via [GitHub Discussions](https://github.com/BenevolentJoker-JohnL/SOLLOL/discussions)

**Most Requested:**
1. LangChain native integration
2. Model fine-tuning support
3. Cost tracking per request
4. Slack/Discord notifications
5. Windows native support

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## Contributing to the Roadmap

**Want to influence direction?**
- 💬 Join [Discussions](https://github.com/BenevolentJoker-JohnL/SOLLOL/discussions) for feature requests
- 🐛 File [Issues](https://github.com/BenevolentJoker-JohnL/SOLLOL/issues) for bugs and improvements
- 🔧 Submit [Pull Requests](CONTRIBUTING.md) for implementations
- 💼 Reach out for funding/partnership opportunities

**Sponsorship/Partnership Opportunities:**
- Advanced distributed inference R&D
- Enterprise features (SSO, audit logs, compliance)
- Cloud provider integrations
- Professional support and consulting

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## Versioning Strategy

**Semantic Versioning (SemVer):**
- **Major (v1.0, v2.0):** Breaking API changes
- **Minor (v1.1, v1.2):** New features, backwards compatible
- **Patch (v1.0.1, v1.0.2):** Bug fixes only

**Release Cycle:**
- **Patch releases:** Weekly (as needed for critical bugs)
- **Minor releases:** Monthly (new features)
- **Major releases:** Quarterly to annually (breaking changes)

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## Success Metrics

**v1.0 Success Criteria:**
- 500+ GitHub stars
- 10+ production deployments (documented case studies)
- <1% critical bug rate
- 90%+ test coverage
- Sub-50ms routing overhead

**v2.0 Success Criteria:**
- Enable 70B models on 3×16GB consumer GPU clusters
- 1000+ GitHub stars
- 50+ production deployments
- Official integrations with 3+ popular frameworks

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## Long-Term Vision (2026+)

**SOLLOL becomes the standard for:**
- Independent AI researchers running frontier models
- Small teams building sovereign AI applications
- Universities conducting distributed AI research
- Hobbyists experimenting with large models

**Impact:**
> "Any engineer with 3 consumer GPUs can run models that previously required enterprise infrastructure."

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**Last Updated:** November 2025
**Current Version:** v0.9.65
**Next Milestone:** v1.0 (March 2025)

For detailed technical architecture and current implementation status, see [ARCHITECTURE.md](ARCHITECTURE.md).
