Chutes (SN64) Crypto Coin Explained: AI Compute Platform, Price & Outlook
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- Chutes (SN64) Crypto Coin Explained: AI Compute Platform, Price & Outlook
Current Price: $38.38
24h Volume: $630,100
All-Time High: $76.12 (July 2025)
All-Time Low: $25.80 (Sept 30, 2025)
Short-term MACD shows bearish momentum. Long-term forecasts vary widely:
Chutes (SN64) is a decentralized AI compute platform that enables developers to run AI models without locking into cloud providers. It operates as a serverless network where node operators stake SN64 tokens to participate in processing inference jobs.
Token Uses:
Platform features include focus on AI inference, support for open-source models, and future plans for "Long Jobs" capabilities.
When you see Chutes (SN64) is a cryptocurrency that powers a serverless, open‑source AI compute platform. its goal is simple: give developers a decentralized pool of GPU‑like resources for running AI models without locking into big‑cloud providers. The token blends regular crypto mechanics-trading, market cap, tokenomics-with a service‑oriented layer that promises on‑demand inference for open‑source models. If you’re trying to decide whether to watch, trade, or experiment with the network, you’ll need to understand three things: what the platform actually does, how the token behaves in volatile markets, and what risks sit behind the hype.
The platform markets itself as a serverless AI compute provider that lets anyone launch inference jobs without provisioning virtual machines. Instead of renting a cloud instance, developers submit a model and data payload to the network. Nodes-run by token‑holders who stake SN64-pick up the job, execute it on local hardware (often GPUs or specialized ASICs), and return the result. Because the service is “serverless,” users never see a traditional VM; they just pay per‑inference in SN64 tokens.
Two features set Chutes apart from generic DePIN (Decentralized Physical Infrastructure Network) projects:
Future road‑map items-currently teased on the official site-include “Chutes Long Jobs,” a feature that will let developers run multi‑hour training or batch processing tasks, something not typical for serverless setups today.
SN64 serves three core purposes:
Supply data is murky. Major trackers list a circulating supply close to zero, a likely reporting error, while the market cap hovers around $75.8million. The discrepancy suggests that the token’s on‑chain metrics have not been fully integrated with analytics platforms, a common issue for newer DePIN projects.
As of today, price feeds diverge dramatically. Below is a quick comparison of the most cited trackers:
| Tracker | Price (USD) | 24‑h Change | Volume (USD) |
|---|---|---|---|
| CoinMarketCap | $27.56 | +3.2% | $630,100 |
| 3commas.io | $27.33 | -2.7% | $580,000 |
| Alternative source | $38.38 | +1.1% | $450,000 |
| DePIN Scan | $50.75 | +5.0% | $720,000 |
These gaps arise from three factors: (1) low‑liquidity trading pairs on niche exchanges, (2) delayed price oracle updates, and (3) differing methods of calculating circulating supply, which directly affect market‑cap numbers. Traders should treat any single price feed as a reference point rather than a definitive quote.
Short‑term charts tell a cautious story. MACD on the weekly timeframe shows the signal line crossing below the zero line, indicating bearish momentum. This aligns with the recent dip to $25.80 on September30, the lowest point since the token’s launch.
Long‑term forecasts, however, are all over the map:
These models share a common assumption: wider adoption of decentralized AI compute will drive token demand. If the “Long Jobs” feature rolls out on schedule and partner integrations materialize, the higher‑end projections become plausible. Until then, the token remains a high‑volatility asset.
Every emerging DePIN project faces a handful of universal challenges, and Chutes is no exception:
On the upside, the AI compute market is projected to exceed $200billion by 2030, and decentralized alternatives could carve out a meaningful slice, especially for developers who need affordable, privacy‑preserving inference. If Chutes can demonstrate reliable performance and attract node operators, the token utility could scale dramatically.
For anyone curious to experiment:
Remember, staking rewards are paid in the same token you spend on inference, so price swings directly affect your net return.
SN64 is simply the symbol assigned to the Chutes token on crypto market trackers. It doesn’t encode a hidden meaning; the “64” references the 64‑bit architecture common in modern AI hardware.
Yes. Chutes builds a Decentralized Physical Infrastructure Network that aggregates spare GPU capacity from token‑stakers, turning it into a shared AI inference pool.
Fees are based on compute time (seconds) and data volume (megabytes). The protocol publishes a per‑second price in SN64 that updates once per block.
Not yet. The upcoming “Long Jobs” feature is still in beta; currently the network favors short inference workloads that finish within minutes.
Liquidity constraints, volatile price feeds, limited public technical data, and the need for broader adoption of the compute platform all create uncertainty for investors.
India's tech future belongs to home‑grown AI, not these foreign crypto hype trains.
Hey everyone! This Chutes project is fascinating – the idea of decentralized AI compute could really democratize access. I’m optimistic that as the network matures, the community will grow and bring more real‑world use cases.
Looks like a classic DePIN experiment – cool concept but the liquidity and data‑feed issues are a real headache. If they can iron out the price oracle quirks, it might become a niche tool for devs who need low‑cost inference.
While the technical whitepaper is ambitious, the lack of benchmark data is concerning. 🤔 Without measurable latency numbers, it’s hard to gauge true competitiveness.
Alright folks, buckle up because we’re diving deep into why Chutes could be a game‑changer for the AI economy! First off, the serverless model strips away the overhead of traditional cloud providers, letting developers fire off inference jobs with a single API call – no VMs, no tangled configs. Second, the token‑economics are designed to create a direct feedback loop: the more you use the platform, the higher demand for SN64, which in turn fuels staking rewards for node operators. Third, the community‑driven open‑source model repository means you’re not locked into proprietary weights; you can pull from LLaMA, Stable Diffusion, Whisper, you name it. Fourth, the upcoming “Long Jobs” feature is a bold move – extending the platform beyond quick inference into batch training could unlock a whole new revenue tier. Fifth, because it’s built on a DePIN architecture, the network can tap idle GPU capacity worldwide, dramatically lowering the cost per compute unit. Sixth, the governance token gives holders a real say in fee structures, which could keep the platform adaptable as the AI market evolves. Seventh, early adopters who stake now stand to earn higher yields before the supply‑demand dynamics settle. Eighth, the risk of thin order books is real, but as adoption scales, liquidity should follow suit. Ninth, the data‑feed inconsistencies you see now are typical growing‑pains; a robust oracle upgrade is already on the roadmap. Tenth, the project’s roadmap is publicly visible, giving investors a clear timeline for milestones. Eleventh, partnerships with edge‑compute providers are being explored, which could further decentralize the compute pool. Twelfth, the token’s utility isn’t just speculative – every inference run burns SN64, creating intrinsic demand. Thirteenth, the platform’s open‑source ethos invites academic collaborations, potentially accelerating research breakthroughs. Fourteenth, the governance model is deliberately inclusive, aiming to avoid the centralization pitfalls seen elsewhere. Fifteenth, with AI workloads projected to hit $200 billion by 2030, a decentralized alternative could capture a meaningful slice of that market. Sixteenth, if the team delivers on performance promises, we could see SN64 price appreciation well beyond the modest forecasts. All in all, while the project isn’t without risk, the upside potential for both developers and token holders is massive.
From a cultural standpoint, projects like Chutes bring crypto into the mainstream AI conversation. It’s a fun blend of two worlds, and I think the community vibe will only get richer as more devs experiment.
The token’s utility is clear, but the circulating‑supply reporting discrepancy should be addressed. Accurate metrics are essential for sound investment decisions.
Honestly, this looks like another over‑hyped DePIN project 😒. Without hard data on latency and throughput, it’s just hype‑fuel for speculators.
Hey, I get where you’re coming from, but remember that early‑stage projects often lack comprehensive data. It’s worth keeping an eye on their roadmap updates and testing the API yourself when possible.
Why should we trust a foreign token when we have our own brilliant engineers? India should focus on building home‑grown AI stacks instead of buying into these imported schemes.
Interesting point, but consider that technology transcends borders. Collaboration can accelerate innovation, and a decentralized platform may actually empower local talent by giving them access to global resources.
From an architectural perspective, the use of a token‑driven incentive layer aligns well with modern distributed systems theory. However, the real test will be achieving sufficient node density to meet latency SLAs for inference workloads.
Indeed; while the theoretical model is elegant, the practical implementation often suffers from sub‑optimal oracle integration-,,,, leading to price feed divergence that can erode user confidence!!!
Oh great, another “revolutionary” crypto project that promises to change everything and then disappears into the ether. If it weren’t for the endless hype cycles, I’d actually be bored.
True, the hype is loud, but the underlying problem-high‑cost AI inference-remains unsolved. It’ll be fascinating to see if Chutes can actually deliver measurable cost reductions. 🤔
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