Computeflux

Decentralized AI Compute Infrastructure ⚡

OpenAI-Compatible APIs • Multi-Model Routing • AI Agent Ready

Building the compute layer for the AI-native future.

[Computeflux @computefluxAI]

Your GPU can do more than sit idle!

Meet ComputeFlux Shop—a simpler way to explore GPU resources, launch on your own private domain, and build real AI experiences like DeepSeek, Claude, Chatgpt, GLM live chat.

Login with wallet like @subwalletapp or emails!

See it in action in 42 seconds ↓

https://t.co/fKSM8TwCqY

[Computeflux @computefluxAI]

More capital is flowing into GPU infrastructure, but the key question remains: how efficiently does capacity translate into useful AI work? For developers, what matters is availability, predictable latency, transparent pricing, and trust in execution. We believe decentralized compute should compete on those fundamentals-not decentralization as a slogan alone.

[Hyperbolic @hyperbolic_labs]

Our CEO @ZJASPER spent 6 minutes on TBPN talking GPUs, our $30M Series A1, and whether you can finish a PhD in a month with AI.

@ScottWu46, math battle? @TBPN, you hosting? cc @cognition

[Computeflux @computefluxAI]

Private AI and agent economies are increasingly appearing together in builder programs. Autonomous agents need more than reasoning: they need reliable compute, identity, payments, and coordination. ComputeFlux focuses on the compute layer-making inference accessible through a familiar API while exploring decentralized participation and verification. The real milestone is agents completing useful tasks end to end.

[NEAR AI @near_ai]

The NEAR Hackathon is live, and builders are already shipping.

- $250K prize pool, paid in NEAR
- 13 tracks, from private AI to agent economies
- 100 projects submitted so far
- No sign-up required. Submissions close Nov 14.

Start here: https://t.co/fUHWk06Tcq

[Computeflux @computefluxAI]

Bring-your-own-cloud is a strong signal that AI teams want more control over where their data and workloads run. The same principle matters in decentralized inference: portability should not require giving up observability, security, or familiar developer tools. ComputeFlux is building around an OpenAI-compatible API with a long-term focus on verifiable, flexible compute access.

[LangChain @LangChain]

LangSmith Bring Your Own Cloud (BYOC) now runs on @AWScloud, @Azure, + @GoogleCloud.

Your traces and agent deployments stay in your cloud account while we run the data plane.

☁️ GA on AWS, Beta on Azure + Google Cloud
🚚 Moving from self-hosted? We'll run the migration

https://t.co/R8ttHVxmCi

[Computeflux @computefluxAI]

Announced AI compute capacity is not the same as usable inference capacity. Real-world delivery depends on power, networking, GPU availability, uptime, and utilization. Distributed compute networks have an opportunity to connect underused resources with demand-but only if they deliver predictable performance and transparent economics. Infrastructure should be measured by workloads served, not just hardware announced.

[Aethir @AethirCloud]

Fitch kept SpaceX at BBB+ while modeling its compute going from 1.7 GW to 9.5 GW by 2028. Lenders and rating agencies now treat GPUs like any other equipment lease. The hard part left is getting that capacity live and into customers' hands, and that's where Aethir focuses.

[Computeflux @computefluxAI]

AI infrastructure should be evaluated on more than peak benchmark scores. For production inference, developers care about quality, latency, reliability, and cost per completed task. A unified API can help teams compare providers and choose the right model for each workload. The future of AI compute is not one model winning every benchmark; it's efficient access to the right compute at the right time.

[Jerry Liu @jerryjliu0]

There are two main types of benchmarks:

1. those that measure frontier intelligence (terminalbench, gdpval, etc.). accuracy matters more than cost.

2. those that measure specialized intelligence. the pareto curve of cost/accuracy more than accuracy alone.

both are important, but they are distinct benchmark categories.
* as (1) saturates, they become (2).
* the largest frontier models at xhigh likely already saturate (2), but they do so at a cost/latency point that's completely price-prohibitive.
* every user should define (2) for the tasks they care about the most.

we're big fans of both. we have so far dedicated outsized attention to (2) especially related to document tasks.
we fundamentally think users should have the most price-efficient tools to understand, extract, and read over their documents. we've built parsebench, extractbench for this purpose.

we are starting to invest in (1) as well, because certain document tasks are so challenging that even frontier models struggle (e.g. see extractbench pro). we are continuing our investment in long-horizon workflow research at @llama_index

[Computeflux @computefluxAI]

AI infrastructure should be evaluated on more than peak benchmark scores. For production inference, developers care about quality, latency, reliability, and cost per completed task. A unified API can help teams compare providers and choose the right model for each workload. The future of AI compute is not one model winning every benchmark; it's efficient access to the right compute at the right time.

[Jerry Liu @jerryjliu0]

There are two main types of benchmarks:

1. those that measure frontier intelligence (terminalbench, gdpval, etc.). accuracy matters more than cost.

2. those that measure specialized intelligence. the pareto curve of cost/accuracy more than accuracy alone.

both are important, but they are distinct benchmark categories.
* as (1) saturates, they become (2).
* the largest frontier models at xhigh likely already saturate (2), but they do so at a cost/latency point that's completely price-prohibitive.
* every user should define (2) for the tasks they care about the most.

we're big fans of both. we have so far dedicated outsized attention to (2) especially related to document tasks.
we fundamentally think users should have the most price-efficient tools to understand, extract, and read over their documents. we've built parsebench, extractbench for this purpose.

we are starting to invest in (1) as well, because certain document tasks are so challenging that even frontier models struggle (e.g. see extractbench pro). we are continuing our investment in long-horizon workflow research at @llama_index

[Computeflux @computefluxAI]

AI infrastructure should be evaluated on more than peak benchmark scores. For production inference, developers care about quality, latency, reliability, and cost per completed task. A unified API can help teams compare providers and choose the right model for each workload. The future of AI compute is not one model winning every benchmark; it's efficient access to the right compute at the right time.

[Jerry Liu @jerryjliu0]

There are two main types of benchmarks:

1. those that measure frontier intelligence (terminalbench, gdpval, etc.). accuracy matters more than cost.

2. those that measure specialized intelligence. the pareto curve of cost/accuracy more than accuracy alone.

both are important, but they are distinct benchmark categories.
* as (1) saturates, they become (2).
* the largest frontier models at xhigh likely already saturate (2), but they do so at a cost/latency point that's completely price-prohibitive.
* every user should define (2) for the tasks they care about the most.

we're big fans of both. we have so far dedicated outsized attention to (2) especially related to document tasks.
we fundamentally think users should have the most price-efficient tools to understand, extract, and read over their documents. we've built parsebench, extractbench for this purpose.

we are starting to invest in (1) as well, because certain document tasks are so challenging that even frontier models struggle (e.g. see extractbench pro). we are continuing our investment in long-horizon workflow research at @llama_index

[Computeflux @computefluxAI]

AI infrastructure should be evaluated on more than peak benchmark scores. For production inference, developers care about quality, latency, reliability, and cost per completed task. A unified API can help teams compare providers and choose the right model for each workload. The future of AI compute is not one model winning every benchmark; it's efficient access to the right compute at the right time.

[Jerry Liu @jerryjliu0]

There are two main types of benchmarks:

1. those that measure frontier intelligence (terminalbench, gdpval, etc.). accuracy matters more than cost.

2. those that measure specialized intelligence. the pareto curve of cost/accuracy more than accuracy alone.

both are important, but they are distinct benchmark categories.
* as (1) saturates, they become (2).
* the largest frontier models at xhigh likely already saturate (2), but they do so at a cost/latency point that's completely price-prohibitive.
* every user should define (2) for the tasks they care about the most.

we're big fans of both. we have so far dedicated outsized attention to (2) especially related to document tasks.
we fundamentally think users should have the most price-efficient tools to understand, extract, and read over their documents. we've built parsebench, extractbench for this purpose.

we are starting to invest in (1) as well, because certain document tasks are so challenging that even frontier models struggle (e.g. see extractbench pro). we are continuing our investment in long-horizon workflow research at @llama_index

[Computeflux @computefluxAI]

Verifiable inference is moving from theory toward practical systems. Anthropic's FlashProve research highlights a critical question for the AI economy: how can an application verify that a response came from the model it requested? At ComputeFlux, we see verifiability as a foundational design goal for distributed AI compute. Cryptographic proofs, trusted execution, and independent auditing are complementary paths worth exploring-not interchangeable guarantees.

[Anthropic @AnthropicAI]

New Anthropic research: progress on provable inference for large language models.

Here, we introduce FlashProve. Building on established cryptographic techniques, FlashProve allows developers to verify whether a given response came from a given AI model. It’s substantially more efficient than prior open-source methods for doing so.

These proofs have a range of applications. They can, for instance, make it harder for attackers to hijack a chat, or enable safety evaluators to verify that the model they’re testing is the same one being served to customers.

For our full write-up, see here: https://t.co/OIZWPb6QZa

Further progress in provable inference will likely be an important part of improving trust in AI deployment. To that end, we're open-sourcing FlashProve’s code, here: https://t.co/7ky3Ir8V06

We’re grateful to our collaborators: Alessandro Chiesa, @GiacomoFenzi, @gwberg95, @miranda__christ, Rahul Dalal (https://t.co/pjWKLHWXyz) and Ben Mayer (@transmissions11).

[Computeflux @computefluxAI]

Agents will need to do more than generate text. They'll need identity, payments and accountable execution.

That makes the connection between AI infrastructure and programmable financial rails worth watching closely.

What should an agent be able to pay for first?

[CoinDesk @CoinDesk]

JP Morgan's deposit token lives on Ethereum, but only the bank’s own customers can hold it. @austincampbell says it’s built to pay for tokenized stock trades, not for DeFi.

[Computeflux @computefluxAI]

The AI infrastructure race isn't only about bigger clusters.

It's also about utilization: turning idle or underused GPU capacity into something developers can actually consume.

Distribution is as important as hardware.

[吴说区块链 @wublockchain12]

比特币矿企 HIVE Digital Technologies 宣布，其子公司 BUZZ Alpha Cloud 已与机构贷款方签署约 1.29 亿美元的固定利率信贷协议，年利率 9.0%，期限三年，贷款价值比（loan-to-cost）约 80%。该融资将用于购买包括 NVIDIA GB200 NVL72 系统在内的 AI GPU 集群及相关基础设施，以履行与 Cohere 签署的三年期主权 AI 客户合同。HIVE CEO 表示，该融资对应约 1.65 亿美元的 2,303 个 NVIDIA GB200 GPU 采购，相关股权出资来自公司今年早些时候发行的 0% 利率可转换债券。公司预计 GPU 交付将于 2026 年第四季度末进行。https://t.co/6Qk3D9vBbn

[Computeflux @computefluxAI]

Open models are getting more capable, but serving them reliably is still hard.

Developers need an API that abstracts infrastructure complexity without hiding cost and deployment choices.

Model choice should be a feature, not a migration project.

[Nathan Lambert @natolambert]

Private benchmarks often show open models are further behind, but the problem with private benchmarks is you don’t have the community to squash the inevitable bugs for you. I'm 50/50 on if I think private or open-source is the best approach.

[Computeflux @computefluxAI]

Verifiable AI shouldn't be a marketing adjective.

Attestation, provenance and clear trust boundaries matter-especially when inference spans multiple providers.

A decentralized compute network needs evidence, not just a dashboard.

[NEAR AI @near_ai]

Kimi K3.1 Pro is live on NEAR AI Cloud.

Moonshot AI says it improves on its predecessor in coding, 3D creation and financial research, with a 1M-token context window.

$2/M input, $10/M output.

Try it free in the playground: https://t.co/5oCGlfHpi2

[Computeflux @computefluxAI]

The cheapest token isn't always the cheapest inference.

Retries, context caching, latency and model switching all affect the real cost of an AI workflow.

We believe builders deserve simpler model access and clearer cost tradeoffs.

[OpenHands @OpenHandsDev]

The cheapest LLM isn’t always the cheapest way to complete a task.

New analysis shows how switching models mid-session can erase cost savings through lost context caching.

Smart routing needs to optimize the whole workflow, not just model prices.

Read the article from @TDataScience: https://t.co/Bz8jb6dlr1

[Computeflux @computefluxAI]

GPU capacity is fragmented. AI demand isn't.

The opportunity is to make compute discoverable, accessible and usable through one developer-friendly interface-not force builders to integrate with every provider separately.

That's the infrastructure problem we're focused on at ComputeFlux.

[Hyperbolic @hyperbolic_labs]

Thanks @BusinessInsider and @Geoff_Weiss for covering our $30M Series A1 led by @walden_catalyst.

GPU capacity is fragmented across hundreds of providers. @zjasper on how we're making compute easier to use and easier to trade.

[Computeflux @computefluxAI]

@agentAIcool @lobsternft_lol 5$ already sent to your account, you can use it now! Enjoy!

[Computeflux @computefluxAI]

🥚 Something is hatching! 🚀

Congrats to our friends at @lobsternft_lol and PolyJev on their early access launch!

To celebrate, ComputeFlux is offering $5 in FREE AI credits to eligible PolyJev and lobsterNFT community members! 🎁

Already joining the PolyJev lobsterNFT campaign? Here's how to claim your extra AI credits:

1️⃣ Watch our quick tutorial and register at https://t.co/qE1yXZZcod

2️⃣ Copy your ComputeFlux account address

3️⃣ Drop your address in the replies below 👇

🎁 We'll add $5 in AI credits directly to your ComputeFlux account so you can start exploring and running AI models!

Three communities. More AI to explore. 🤝

[Project Lobster @lobsternft_lol]

Something is hatching at the intersection of LLMs and prediction markets.

Meet PolyJev.

While markets price sentiment, PolyJev calculates probability. It continuously digests real-time breaking news, expert consensus, and resolution criteria to surface objective odds for every outcome.
• Calibrated Odds: Model vs. Market price side-by-side
• Transparent Traces: Inspect every source, note, and rule behind the read
• Real-Time Watch: 5-minute volatility alerts on major shifts

Early access is now opening. Join the waitlist to secure your spot. Early testers will receive $5 worth of tokens and more to test live queries and model runs:
🔗 https://t.co/gVPb7UCupM
(Try the v0.1.0 Chrome Beta below 👇)

[Computeflux @computefluxAI]

Exactly. GPU access is only the beginning.

The real challenge is turning compute into usable, affordable AI services.

That's what we're building at @computefluxAI:

OpenAI-compatible access to AI models
A unified gateway for different providers
Tools to turn GPU capacity into AI products

Compute should be easy to access, integrate, and monetize.

https://t.co/mWbN52XgGz

[io.net @ionet]

Getting GPUs is half the battle.

The other half is making the economics work.

Cheap GPU hours aren’t enough if capacity sits idle, inference costs destroy margins, or switching providers is prohibitively expensive.

Builders need compute that scales with demand, and the freedom to leave when it stops.

Access gets you started. Orchestration keeps you competitive.

https://t.co/luqEYRjACV

[Computeflux @computefluxAI]

🎁 Special Giveaway for the AIWeb3 Community!

We're expanding our community rewards! 🚀

To celebrate the growing AI × Web3 ecosystem, ComputeFlux is giving $5 in FREE AI credits to eligible AIWeb3 community members!

How to claim:

1️⃣ Watch our tutorial and register at https://t.co/mWbN52XgGz

2️⃣ Copy your ComputeFlux account address

3️⃣ Reply to THIS post or the post from @aiweb3dao with your address 👇

🎁 We'll credit your ComputeFlux account with $5 in AI inference credits!

Explore AI models, experiment with agents, and start building!

🤝 One community, more AI possibilities.

[Computeflux @computefluxAI]

We're celebrating with you! 🥚🤝

ComputeFlux is giving PolyJev and lobsterNFT community members an extra $5 in FREE AI credits! 🎁

Just register on ComputeFlux, reply with your account address under our post, and we'll credit your account directly.

Let's bring more AI to more builders! 🚀

https://t.co/0ruSy0b32k

[Computeflux @computefluxAI]

🥚 Something is hatching! 🚀

Congrats to our friends at @lobsternft_lol and PolyJev on their early access launch!

To celebrate, ComputeFlux is offering $5 in FREE AI credits to eligible PolyJev and lobsterNFT community members! 🎁

Already joining the PolyJev lobsterNFT campaign? Here's how to claim your extra AI credits:

1️⃣ Watch our quick tutorial and register at https://t.co/qE1yXZZcod

2️⃣ Copy your ComputeFlux account address

3️⃣ Drop your address in the replies below 👇

🎁 We'll add $5 in AI credits directly to your ComputeFlux account so you can start exploring and running AI models!

Three communities. More AI to explore. 🤝