Decentralized AI Reasoning: A Comprehensive Analysis of Four Core Challenges - From Petals to Darkbloom, Who Can Truly Implement it?

Written by: @KSimback

Translated by: AididiaoJP

Scenario Hypothesis: What Happens When Cutting-Edge Models are Banned?

The year is 2026, October, just four months away from now. GLM-6 has just been released, surpassing Fable-5.1 (the censored re-release of a banned model) in mainstream benchmark tests and tying with Mythos. The U.S. government is unable to directly shut it down, so it issues a series of prohibitions: prohibiting any provider from offering the GLM-6 model, updates, inference services, operational deployment, or technical support within the U.S. or to U.S. citizens.

Amazon Bedrock, Google Vertex, and Microsoft Azure quickly announced their compliance, refusing to host the model for enterprise customers. Major aggregation platforms such as OpenRouter, Vercel, Cloudflare, and TogetherAI also agreed not to list it. GitHub cleared all relevant traces from its platform. Hugging Face, as the last holdout, eventually removed all downloads related to GLM-6 models.

This scenario, while not the ideal outcome we hope for, is entirely reasonable in a world where AI models advance exponentially while policy-making crawls along like a snail.

This outcome, or another scenario where cutting-edge AI is still monopolized by a few centralized entities, is the fundamental reason why decentralized AI is so important.

This article is a companion piece to the author’s previous introductory guide, “Proof of Useful Work,” adopting the same pragmatic approach and focusing on another key corner of crypto-AI (with some overlapping areas). The author deeply dissects the challenges that decentralized AI must solve, projects in pursuit, due diligence frameworks, and personal judgments made after in-depth research.

Why is Decentralized Reasoning Inevitable?

Following the above scenario, you may have already associated it with decentralized reasoning. If you haven't figured it out yet, let’s continue our derivation.

Once the weights of the GLM-6 model are released, copies will instantly spread across the internet—no ban or remedial measures can eliminate the thousands of copies that already exist. These copies will be serviced within the decentralized reasoning network, as there is no central authority that can act upon them, and no single node being banned can incapacitate the entire network.

I want to clarify one point at first: I am not arguing about whether this is a good or bad thing. If a newly released open-weight model could cause serious harm due to misuse, I would never suggest that people turn a blind eye. What I want to emphasize is that the model will eventually be accessed by those who do not want to be censored; this is inevitable.

This is the core premise of decentralized reasoning—it is a hedge against intelligence censorship, whether that censorship comes from the government or cutting-edge laboratories. Other selling points, such as cheaper tokens, verifiable reasoning, privacy protection, etc., are all secondary. The core risk mitigation is singular: alleviating censorship risks.

Decentralized Reasoning is Really Difficult: Four Major Challenges Ahead

For most startups, solving one or two challenges is already a huge undertaking. However, decentralized reasoning projects must tackle four genuinely tricky problems at once. How each project handles these issues is key to distinguishing substance from bubbles, alpha from noise.

Challenge One: Models Too Large to Run on a Single Machine

The core idea is to build a GPU cluster (swarm) and use pipeline parallelism to serve the models that users genuinely want. In simple terms, each node holds only a small slice of the model weights and its part of the KV-cache, small enough to fit into consumer-grade 3090/4090 graphics cards, or even higher-spec H100s. By combining enough nodes, it is possible to host large models like GLM.

Petals demonstrated the viability of this method in 2022 using BLOOM-176B on consumer-grade GPUs in a BitTorrent-style swarm, but at that time the speed was only about 1 token per second. Clearly, this speed is completely unusable, so subsequent innovations focused on how to make the model run faster.

The real bottleneck is the network. Within data centers, GPUs communicate at TB-per-second speeds through NVLink; while on the public internet, round trip latency (RTT) can be tens of milliseconds. The decoding process is sequential, and a naive swarm incurs one network round trip for each generated token.

The most common solution is speculative decoding: a small and cheap draft model proposes K candidate tokens, and the large sharded model verifies those K tokens in a single pipeline pass, then retains the longest matching sequence. This way, an expensive network traversal can yield several tokens rather than just one.

Currently, levels of about 30-40 tokens per second have been achieved on real internet connections, showing significant progress, but still not fully validated at large scales and speeds that users genuinely need. This is a problem that requires real hardcore engineering capabilities.

Note: Serving inference is about more than just FLOPs

There’s a common pitfall when comparing any swarm method to cloud-hosted models: everyone only looks at tokens per second, thinking that’s all there is to it.

But production-level inference must excel at many things that have nothing to do with raw computation power:

• Balancing First Token Time (TTFT) with inter-token latency
• Distinctions between prefill and decode stages (which have completely opposing hardware requirements)
• Placement and transfer of KV-cache
• Streaming, continuous batching, and utilization under mixed loads
• Long context behavior, cold starts, and model warm-ups
• Node churn

Due diligence points: When projects reference throughput numbers, always ask what they are competing against. Centralized vLLM or SGLang deployments (using disaggregated prefill and continuous batching) are the real benchmarks, and this benchmark is getting faster every quarter. "We reach 30 tokens per second on the internet" sounds impressive, but may still lack competitiveness.

Challenge Two: Proving You Really Got the Model You Paid For

If you don’t trust the node, how do you know it’s actually running the claimed model, and not secretly switching to a cheaper quantized version? Particularly in networks involved in mining tokens, providers can easily "game the system,” appearing to service the actual model while running something much cheaper.

There are currently five mainstream countermeasures:

• ZKML: Zero-knowledge proofs for forward transmission. Cryptographically unassailable but has an overhead around 10,000 times that of native systems. The Llama-3 model takes about 150 seconds to generate one token. Cutting-edge scales won’t be feasible in the short term.
• OpML: Outputs come with margins, opening a challenge window, with fraud-proof splitting the dispute into steps to be re-run by an arbitrator. Nearly native speed, but finality waits for the window period, and there exists a "verifier’s dilemma” (if verification costs exceed the value of catching fraud, nobody verifies).
• Deterministic re-execution: Making inference byte-level reproducible, requiring only byte comparisons for disputes. The overhead is less than 2%, secured by restaked ETH.
• Statistical fingerprints: Cheaply hashing or sampling computations, catching most cheating most of the time. Not absolutely accurate but fast and suitable for heterogeneous GPUs, which is needed for permissionless swarms.
• Live-weight proofs: Directly sampling the tensors actually resident during service runtime, comparing them with the approved model’s manifest. Verifying "what was loaded," not "what was output," with an overhead of only about 0.1%. This represents a truly different approach.

The real trade-off is: you can only have two of these three—cryptographic integrity, low latency, cost efficiency. ZKML achieves integrity but sacrifices latency and cost; other methods achieve latency and cost but can only satisfy economic or statistical integrity.

Due diligence points: Clearly ask the project which method they are using, why, and how this trade-off affects the final product.

Challenge Three: How to Truly Keep Prompts Confidential?

Proving the output is correct is completely different from concealing the input. In a sharded swarm, each node must decrypt activations to compute—encryption only protects the transmission path but does not safeguard the node itself.

Transformer activations are actually very easy to reverse-engineer. A CCS 2025 paper shows a reconstruction accuracy exceeding 90% in predicting input prompts from intermediate activations. The "Hidden No More" paper at ICML 2025 achieved near-perfect recovery, defeating the noise-and-permutation defenses commonly used in swarms.

The only robust fix currently is a heavier sequence-sharded scheme, which no one has truly rolled out in the consumer GPU camp yet, hence it remains a largely unresolved issue.

A swarm can claim, "No node holds the entire model," yet still leak each prompt to any node in the path. "No node holds the model" has never been a privacy property.

What can truly provide privacy are hardware or mathematical methods, not network topology. TEEs (Trusted Execution Environments)—such as Phala's solutions on GPUs, Darkbloom on Apple Silicon, and Venice’s Pro mode—shift trust to hardware roots and perform attestation.

Fully homomorphic encryption (FHE) can compute directly on ciphertext without trusting anything, but is currently prohibitively expensive for large models.

Due diligence points: A project either genuinely possesses one of these solutions or it has no privacy, regardless of how the landing pages are packaged.

Important reminder: Private does not equal trustless. TEEs do not eliminate trust; they merely shift trust from node operators to hardware vendors, firmware chains, attestation services, and enclave implementations.

The real question is: whose root of trust are you willing to accept? Chip manufacturers? Restaked validator pools? TEE networks? Or pure mathematics?

Challenge Four: How to Build a True Bilateral Market?

The first three are technical challenges, while the fourth is a business challenge.

For decentralized reasoning networks serving open-weight models, who is the Ideal Customer Profile (ICP)?

Most ordinary consumers are currently deriving immense value from subscription plans—access to a wealth of intelligence for $20-200 per month. These subsidy plans may disappear or be limited in the future, but selling on-demand API-based inference to consumers is very challenging today.

Businesses also will not become major buyers in the short term. Perhaps this will change in the future, but don’t expect it to happen quickly.

The two real remaining user types are: 1) startups and enterprises embed reasoning into their product stacks and inherently need API plans; 2) autonomous AI agents seeking their own inference capabilities.

The startup category is a growing market, a niche that could lead to significant revenue capture, but there are clear limits to value capture in the short term. AI agents as buyers are more speculative—still requiring someone to pay in the short term.

That’s the problem: how to aggregate a meaningful supply of the models people genuinely want, while the target user group is unlikely to be the big spenders on the network?

The only currently feasible area is decentralized GPU providers. Projects like io.net, Akash, Render, Aethir, and Nosana have been doing this for years, renting out entire GPU or per-node model capacity to payers through token-coordinated markets. This is precedent-based.

Due diligence points: Clearly ascertain the project’s ICP and how they plan to acquire target users while satisfying the supply side. If everything is built on speculative token appreciation expectations, that’s a clear signal.

Who is Truly Solving These Challenges? A Survey of Mainstream Projects

There are many projects classified as "decentralized reasoning," but most do not equally solve all four challenges, instead having varying focuses.

Petals: The absolute pioneer in decentralized reasoning. Proved in 2022 that BLOOM-176B could run on consumer-grade GPUs in a BitTorrent-style way, which is conceptually significant, but didn’t solve incentive, privacy, and monetization issues. Essentially a "Petals architecture + token" project, which is likely a larp.

Dolphin Network: The team behind the uncensored open models in the Dolphin series (Hugging Face downloads exceed 5 million). Initially driven by a real user demand, packaging the network around that. The technical highlight is live-weight proofs (0.1% overhead), combined with logprob fingerprints, software integrity checks, and account-level bonding. Has generated over 3.2 billion tokens, with ongoing bandwidth of about 9400 t/s, representing product-first and execution-oriented values.

Inference.net (formerly Kuzco): One of the most mature attempts at executing verification for wild models. Its unique mechanism LOGIC captures model substitution based on logprob statistical tests, having been in production for about 18 months, with a fleet size of thousands of GPUs, making it one of the few with both verification primitives and a real operational history.

Morpheus: A decentralized routing and reward layer, providing an OpenAI-compatible API + intelligent agent wrappers. The technical highlight is TEE-supported provider validation (Intel TDX + NVIDIA GPU attestation have been launched). Continuous attention is needed for MOR emissions and evidence of real external demand.

Chutes (Bittensor subnet 64): The user side is an OpenAI-compatible API, with the backend deployed via Docker to Bittensor GPU miners. Clear distribution and scale advantages, but still lacking in verification and privacy.

c0mpute: A new native project on Solana, the Shard engine splits cutting-edge models to consumer-grade GPUs. Real demonstrations of GLM-5.2 744B and gpt-oss-120B have been made public (30-40 t/s). Technical artifacts are verifiable, but still very early-stage (the repository was just launched a few days ago, founder anonymous, token is a micropump.fun).

Parallax (Gradient Network): A P2P distributed LLM reasoning framework supporting pipeline parallel sharding across consumer-grade GPUs and Apple Silicon, allowing individuals or small organizations to run "sovereign clusters." Backed by strong institutions (Pantera and Multicoin leading a $10 million seed round), but privacy schemes remain unclear.

Darkbloom: Allows users to convert idle Mac computing power into a private reasoning market. Each Mac runs the entire model, ensuring privacy through Secure Enclave attestation. It does not follow the sharded swarm route, with a rigorous attestation stack. Has moved from research preview to public alpha, with real traction worth monitoring (decentralization doesn’t necessarily have to be tokenized).

MeshLLM: A permissionless P2P reasoning mesh built by a team associated with Block introduced by Jack Dorsey. It discovers nodes based on Nostr, with no central server, closer to BitTorrent than Bittensor. Protocol-first, no token, censorship-resistant.

Venice and its resale ecosystem: A model seeking PMF and feasible business models across the entire field. It is itself a centralized yet privacy-layered consumer agent that has effectively solved some challenges. Around it has formed a resale sub-ecosystem including UsePod, AntSeed, Surplus Intelligence, mainly doing demand aggregation and settlement, rather than directly offering decentralized computing power.

Where are the Wins and Losses for Decentralized Reasoning?

Cost advantages only hold when viewing latency and throughput separately. They are two different products; decentralization acts as a tax on one and a feature on the other.

Centralized scenarios where it clearly outperforms (where decentralization is a tax): ChatGPT-style interactive chatting, real-time coding agents, low-latency voice, high-frequency tool calls, strict p95 latency SLAs for enterprises, competitive latency services for cutting-edge models.

Decentralized scenarios where it might win (advantages from supply aggregation): Synthetic data generation, offline evaluation, bulk embedding, batch RAG, long-term agent research tasks, image and video generation queues, non-urgent open model reasoning (marginal cost of idle hardware close to zero).

Simple framework: When latency is important, decentralization is a tax; when throughput is important, decentralization can become a supply aggregation advantage.

Hidden Long-Term Value: Data Loops

Decentralized reasoning networks can also collect vast amounts of valuable data—synthetic training data, preference data, agent traces, evaluation outputs, fine-tuning data, RL environments, tool usage trajectories, etc. This data can feed back into decentralized training networks (such as Nous Psyche, Prime Intellect, Gensyn style projects), producing updated open-weight models that subsequently flow back into the reasoning network.

In the long run, this is not just a gamble on "decentralized training" or "decentralized reasoning," but a closed loop: reasoning generates trajectories → trajectories become training data → training updates models → updated models flow back into reasoning.

The best projects will take this loop as their core strategy, and future training and reasoning projects will further merge.

Practical Due Diligence Checklist: Just Answer These Seven Questions

• Is it genuinely decentralized? Specifically, at which layers? (Many just label it so because there's a token)
• Can you trust that the output comes from the model you paid for? (Determinism, proof, fingerprints, or nothing at all)
• After deducting token and coordination costs, is it actually cheaper than centralized? (In production, not theoretical)
• Is the prompt truly hidden from the operators? (TEE/FHE counts, mere sharding does not)
• Can the system remain stable when nodes are unreliable and dispersed over the internet?
• Is anyone really paying for it, and are they unable to buy it at a cheaper price centralized?
• Does the team possess real AI technical capabilities? (The most crucial point)

Additional advice: Watch out for "elegant technical solutions" that lack credible distribution plans.

My Final Judgment

I am generally bearish on categories that only appeal to crypto natives (TAM seems limited to me). I would prefer to see projects that also appeal to non-crypto users, hiding crypto mechanisms behind the scenes.

Decentralized reasoning is one of the few tracks in crypto with real breakthrough potential—everyone needs reasoning, and it can serve just like traditional providers, even achieving seamless experiences through platforms like OpenRouter. The key lies in cost, performance, and privacy.

I recommend supporting projects that can clearly articulate which layer they have decentralized and clearly understand who their buyers are. Steer away from those that treat "decentralized AI" merely as a slogan, followed by a token.

Disclosure: The original author holds tokens from some of the projects mentioned in the text and has not been influenced or compensated by any project, with judgments being personal opinions.