Zero-knowledge proofs (ZKPs) are no longer just a cryptographic curiosity—they're rapidly becoming a foundational technology for privacy, scalability, and trustless computation across web3 and beyond. At the heart of this transformation lies the zkVM (Zero-Knowledge Virtual Machine), a powerful tool that enables developers to prove the correctness of arbitrary computations without revealing any underlying data.
In this first installment of a three-part series, we'll explore what zkVMs are, how they work, and why they matter. We’ll break down complex concepts like ZKPs, zkSNARKs, zkSTARKs, and the relationship between zkVMs and zkEVMs—all in clear, accessible language. By the end, you’ll have a solid foundation for understanding one of the most promising frontiers in modern cryptography.
Understanding Zero-Knowledge Cryptography
“Within the next 5 years, we will be talking about applications of zero-knowledge protocols the way we talk about applications of blockchain protocols.”
— Jill Gunter, Espresso Systems (2021)
Since 2021, the zero-knowledge (ZK) landscape has evolved from niche academic research into a vibrant ecosystem powering real-world blockchains, privacy tools, and scalable infrastructure. Projects like Starknet and zkSync Era have brought ZK rollups into mainstream use, yet much of the technology remains poorly understood—even within the crypto community.
The reason? Information is fragmented. On one end, technical papers drown readers in mathematical formalism. On the other, oversimplified analogies (like “finding Waldo”) fail to convey real mechanics. The truth is: you can’t grow zero knowledge with zero knowledge.
That’s why this series exists—to demystify zkVMs and equip developers, builders, and enthusiasts with a clear mental model of how these systems function, how to evaluate them, and where they’re headed.
👉 Discover how zero-knowledge technology is shaping the future of secure computing.
What Are Zero-Knowledge Proofs (ZKPs)?
At its core, a zero-knowledge proof (ZKP) allows one party (the prover) to convince another (the verifier) that a statement is true—without revealing why or how it’s true.
Imagine proving you know a password without typing it. Or verifying a financial transaction was computed correctly without seeing the account balances involved. That’s the power of ZKPs.
More technically:
- ZKPs prove knowledge of data or the result of a computation.
- They do so without exposing inputs or intermediate steps.
- The proof itself is a cryptographic artifact derived from algebraic transformations of the computation.
One of the most compelling features of ZKPs is succinct verification: it often takes less time to verify a proof than to re-run the original computation. This makes ZKPs ideal for scaling blockchains and securing sensitive processes.
Key Types of ZKPs: zkSNARKs vs zkSTARKs
Two major families dominate today’s ZKP landscape:
zkSNARKs – Zero-Knowledge Succinct Non-Interactive Argument of Knowledge
- Require a trusted setup phase to generate public parameters.
- Generate small proofs that are fast to verify.
- Used by zkSync, Scroll, Linea, and Jolt.
- Best for applications prioritizing low verification cost and compact proof size.
zkSTARKs – Zero-Knowledge Scalable Transparent Argument of Knowledge
- No trusted setup required—uses public randomness.
- More transparent and trustless by design.
- Generate larger proofs but scale better with large datasets.
- Used by Starknet, Risc Zero, Succinct Labs (SP1), and Lita.
- Ideal for high-assurance environments where decentralization and auditability are critical.
⚠️ Note: All STARKs are SNARKs (by definition), but not all SNARKs are STARKs.
For deeper exploration, refer to foundational writings by Axiom’s Yan Zhang and Yi Sun, or explore Ventali Tan’s curated Awesome ZK GitHub repository.
What Is a zkVM?
A virtual machine (VM) runs programs. A zkVM does the same—but cryptographically proves every step was executed correctly, without revealing any private inputs.
Think of it as a sandboxed computer that generates mathematical proof of its own integrity. This enables:
- Trustless off-chain computation.
- Private smart contracts.
- Verifiable AI inference.
- Secure data processing in untrusted environments.
Technically, a zkVM is a system that:
- Executes code.
- Produces an execution trace.
- Converts that trace into a zero-knowledge proof.
- Allows third parties to verify correctness—without rerunning the program.
Most zkVM implementations include:
- A compiler (to translate high-level code into VM-compatible bytecode).
- An execution engine (to run the program and generate traces).
- A prover (to generate ZK proofs).
- A verifier (to check proofs efficiently).
The choice of instruction set architecture (ISA) and proof system (SNARK/STARK) shapes performance, compatibility, and developer experience.
zkVM vs zkEVM: Key Differences
While both leverage zero-knowledge proofs, there’s an important distinction:
| Feature | zkVM | zkEVM |
|---|---|---|
| Purpose | General-purpose ZK computation | Ethereum-compatible smart contract execution |
| Compatibility | Any language/ISA | Solidity & EVM bytecode |
| Use Case | Cross-chain apps, private compute | Scaling Ethereum dApps |
A zkEVM is a specialized zkVM designed to execute Ethereum Virtual Machine (EVM) code with ZK proofs. It maintains full compatibility with existing Ethereum tooling while enabling Layer 2 scaling via rollups.
In contrast, general-purpose zkVMs support broader instruction sets (e.g., RISC-V) and can run C, Rust, or even machine learning models—making them ideal for non-Ethereum use cases.
👉 See how next-gen virtual machines are redefining trustless computation.
How Does a zkVM Work? The Process Flow
Here’s a simplified view of how a zkVM turns code into verifiable truth:
1. Compiler Stage
Programs written in languages like Rust or C are compiled into machine code based on the VM’s ISA (e.g., RISC-V).
2. VM Execution
The zkVM runs the program and generates an execution trace—a detailed record of each computational step.
3. Arithmetization
The trace is converted into mathematical constraints using schemes like:
- R1CS (used in Groth16)
- PLONKish (used in Halo2)
- AIR (used in Plonky2/3)
This maps code logic into polynomial equations.
4. Polynomial Commitment
Using a Polynomial Commitment Scheme (PCS), the prover creates a “fingerprint” of the polynomials—allowing verification without exposing raw data.
5. Interactive Oracle Proof (PIOP)
The prover and verifier engage in a probabilistic challenge-response protocol. Random values test whether polynomials satisfy constraints.
6. Non-Interactive Transformation
Via the Fiat-Shamir heuristic, the interactive process becomes non-interactive—enabling standalone proofs suitable for blockchain use.
7. Proof Generation & Verification
The final proof is submitted to a verifier, who checks it against public parameters. If valid, the computation is accepted as correct.
✅ In short: A zkVM proves that, given a program and output, some input must have produced that result when executed.
How to Evaluate zkVMs: A Framework
Not all zkVMs are created equal. Choosing the right one depends on your application’s priorities. We break evaluation into two categories:
🔹 Baseline: Reliability Criteria
These are non-negotiable for production use.
Correctness
Ensures the system behaves as intended:
- Soundness: False statements cannot be proven.
- Completeness: True statements can always be proven.
- Zero-Knowledge: Inputs remain hidden.
Security
Measured in "bits of security"—a statistical guarantee against breaking soundness or privacy. Higher bits = lower risk.
Trust Assumptions
- Trusted setups require faith that setup participants deleted secret randomness.
Trustless systems (like STARKs) eliminate this need.
→ Less trust = higher security posture.
🔹 Performance: Operational Metrics
Speed
Wall-clock time to generate a proof. Critical for latency-sensitive apps like DeFi trading.
Efficiency
Resource usage during proving:
- Core-time: CPU seconds consumed (correlates with energy cost).
- Space: RAM usage.
Efficient provers reduce operational costs—especially at scale.
Succinctness
Refers to:
- Proof size (smaller = cheaper to store/send).
- Verification time and memory usage.
Highly succinct proofs enable lightweight verification on-chain.
The zkVM Trilemma
You rarely get all three performance traits at once:
| Optimize For | Trade-offs |
|---|---|
| Speed | Higher resource use; larger proofs |
| Efficiency | Slower generation; less succinct |
| Succinctness | Longer proving time; higher overhead |
This is the zkVM trilemma: improving one dimension often sacrifices another. The optimal solution depends on your use case:
- Finance apps → prioritize speed.
- Cloud services → optimize efficiency.
- Blockchain rollups → favor succinctness.
👉 Explore platforms leveraging zkVMs for secure, scalable solutions.
Frequently Asked Questions (FAQ)
Q: Can zkVMs run any program?
A: Yes—within limits. General-purpose zkVMs support languages like Rust and C via ISAs like RISC-V. However, proving complex programs can be slow or expensive.
Q: Are zkVMs only useful for blockchains?
A: No. They’re valuable anywhere verifiable computation matters—private cloud computing, AI auditing, identity systems, and secure enterprise workflows.
Q: Do I need cryptography expertise to use a zkVM?
A: Not necessarily. Modern toolchains abstract away complexity, letting developers write code in familiar languages while the backend handles proof generation.
Q: How do zkVMs scale blockchains?
A: By processing transactions off-chain and submitting compact proofs on-chain—reducing gas fees and congestion while preserving security.
Q: Is proving always slower than running code normally?
A: Typically yes—but verification is much faster. This asymmetry enables scalable architectures where many users verify cheaply what one prover computes at higher cost.
Q: What makes zkEVM different from Ethereum?
A: zkEVM runs the same code as Ethereum but generates ZK proofs to validate batches of transactions off-chain—then posts only the proof to L1 for final settlement.
This concludes Part 1 of our series. In upcoming articles, we’ll dive into zkVM design trade-offs and custom instruction set architectures—unlocking deeper insights into performance optimization and real-world deployment strategies.