Position
Golem is the oldest project in this library's decentralized compute coverage:
- Founded 2017 — predates most DePIN and decentralized compute projects by years.
- GLM token — utility token on Ethereum (ERC-20) and Polygon.
- Generic compute — not specialized to a single workload type; any embarrassingly parallel job works.
- Architecture: provider/requestor — the standard peer-to-peer model Golem helped pioneer.
Compared to the rest of this library's compute coverage:
- Akash: Kubernetes-style container deployments; Golem is more task-oriented.
- Phala: TEE-confidential; Golem has no TEE component.
- Render: GPU rendering/ML-specific; Golem is generic.
- Bittensor: specialized AI subnets; Golem is general-purpose.
Architecture
Provider / Requestor model
Golem implements the classic two-sided marketplace:
- Providers: operators running the
yagnadaemon and sharing CPU/GPU resources. Each provider runs inside a sandboxed VM (was Docker, evolved to unikernels). - Requestors: actors who submit jobs (Golem calls them "activities") to the network and pay in GLM.
- Yagna daemon: the core software both providers and requestors run. Implements the Golem protocol, market, and payment settlement.
Market and settlement
The Golem market matches provider offers to requestor demands:
- Requestor posts a demand (specifies compute requirements and price).
- Providers with matching offers are selected.
- Job runs on the provider's infrastructure.
- Provider invoices the requestor.
- Payment settles in GLM (via Polygon or Ethereum).
Payment platforms
Golem supports settlement on: - Ethereum Mainnet — expensive but highest security. - Polygon — lower fees, sufficient for most microtransactions.
Sandbox
Golem pioneered the execution sandbox:
- Early versions: Docker-based.
- Current: Unikernel-based VMs for stronger isolation between requestor workloads.
- Each job runs in its own execution environment; providers can't access other providers' data or each other's jobs.
GLM token
- Standard: ERC-20 (Ethereum)
- Networks: ETH Mainnet and Polygon
- Supply: 1,000,000,000 total (1:1 migration from legacy GNT)
- Utility: the sole currency for paying providers
GNT → GLM migration
Golem's original token was GNT (2017). Migration to GLM began in late 2020 and is ongoing. 1:1 conversion ratio.
Use cases
Batch computation
The canonical Golem use case: embarrassingly parallel batch jobs where provider trust doesn't matter.
- Examples: rendering farms, video encoding, distributed computing (BOINC-style science workloads).
- Current fit: moderate. Golem handles this but has more competition now.
Web2 partnership
Notable 2026 development: Salad.com partnership — a GPU cloud platform that partnered with Golem to test Golem's network for enterprise-grade AI and rendering workloads, with settlements in GLM.
This represents Golem's path to real-world adoption: engineering trials with established Web2 GPU cloud platforms.
Token utility
For token-economic analysis: GLM is one of the earliest DePIN tokens with continuous mainnet operation since 2017. Longest track record of any decentralized compute token.
Comparison
| Dimension | Golem | Akash | Phala/dstack | Render | io.net |
|---|---|---|---|---|---|
| Founded | 2017 | 2020 | 2022 | 2017 | 2023 |
| Token | GLM (ERC-20) | AKT (Cosmos) | PHA (Substrate) | RNDR (Solana) | IO |
| Focus | Generic batch | K8s containers | TEE compute | GPU rendering | GPU ML |
| GPU support | Limited | Growing | H100 CC | Strong | Strong |
| Confidential | No | No (roadmap) | Yes (TEE) | No | No |
| TEE roadmap | No | 2026 | Production | No | No |
| Status | Maintain | Active | Active | Active | Active |
Trade-offs
Strengths
- Battle-tested: 8+ years of mainnet operation. Longest track record in decentralized compute.
- Generic model: any parallelizable workload fits. Not constrained to containers or specific GPU architectures.
- Simple trust model: the provider sees the data. For non-sensitive workloads, this is fine.
- Polygon settlement: low fees for high-volume microtransactions.
Limitations
- No confidential computing: provider is fully trusted. For AI inference where prompt privacy matters, this is a dealbreaker.
- Smaller network: far fewer providers than Akash or Render. Limited GPU inventory.
- No frontier AI story: not optimized for LLM inference or training in the way modern GPU networks are.
- Slow evolution: newer projects (Phala, Akash 2026 GPU, Bittensor) move faster.
- Developer tooling: Yagna is Rust-based; less mainstream than Kubernetes/Docker for the average cloud developer.
Attack surface
| Attack | Mitigation |
|---|---|
| Provider reads requestor data | No mitigation — full trust. Don't use for sensitive workloads. |
| Provider runs malicious code | Sandbox (unikernels). Requestor verifies results. |
| Provider claims work without doing | Reputation system and scoring. |
| Payment fraud | Escrow via market smart contract. |
| Sybil providers | Identity and reputation require history. |
| Smart contract exploit | Audited contracts. Time-locked admin. |
Primary sources
- Golem — golem.network
- Documentation — docs.golem.network
- GitHub — github.com/golemfactory/golem
- GitHub (yagna) — github.com/golemfactory/yagna
- GLM token — golem.network/glm
- Salad partnership (Jan 2026) — blog.golem.network
Page history
- Updated
- 2026-06-09
- Origin
- Compiled from the ungovernable.tech research notes.
- Improve this page
- Suggest an edit — issues and pull requests are welcome.