Decentralized AI Compute Infrastructure ⚡ OpenAI-Compatible APIs • Multi-Model Routing • AI Agent Ready Building the compute layer for the AI-native future.
Joined April 2026
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DeepSeek V4 Flash Vision is already live on ComputeFlux 👁️
No new SDK. No custom adapter.
Just one OpenAI-compatible endpoint connected to DeepSeek Harness.
This 16-second demo goes from configuration to image recognition.
Beta users get free credits:
test.computeflux.ai/activiti…
Agent payments are moving from concept to real pilots.
The next step is equally important: agents need reliable, programmable access to inference.
Budgets, permissions, usage tracking, and flexible model access will matter as much as the payment rail.
That is the future we are building toward at ComputeFlux.
Mastercard opens early access to AI agent payments following pilots with Coinbase, Base and Cloudflare theblock.co/news/business/20…
TEE inference is not a niche feature.
Agents are moving from generating text to touching:
credentials
wallets
private company data
financial transactions
At that point "trust us, your prompt is safe" isn't enough.
AI needs the same evolution crypto went through:
Don't trust. Verify.
ComputeFlux is being built toward confidential and verifiable inference, not just cheap model access.
Today's free beta covers multi-model access. TEE-backed execution is on our roadmap.
Fast AI is useful.
Verifiable AI is much more important.
GLM 5.3 Flash is live on Phala—served inside a TDX-attested GPU TEE.
Private, verifiable inference for coding, long-horizon agents, visual understanding.
phala.com/models/z-ai/glm-5.…
961.6 tokens/s is an impressive inference milestone.
But raw speed is only one part of production AI. Developers also need predictable latency, stable endpoints, and sustainable costs.
The next infrastructure breakthrough is making powerful inference easier to access, not just faster to benchmark.
Congrats to the @EagleCorp team on EAGLE-4, their fastest yet, hitting 961.6 tokens/s on Qwen3.8-27B!
Proud to be an official launch partner, with EAGLE-4 coming soon to Hyperbolic.
ALT Eagle and Hyperbolic logos, Launch Partner, Accelerating the future of AI inference.
Hot take:
The model is becoming the commodity.
The harness is becoming the product.
Today it's Claude.
Tomorrow it's GPT.
Next week an open model may win your workload.
Rebuilding your stack every time the leaderboard changes is insane.
That's exactly why we built ComputeFlux around a model-agnostic interface: one API, multiple models, and the freedom to switch when the leaderboard does.
Our beta is already live - and it's free to try.
Don't marry the model.
Own the layer above it.
Going from 20% to 43% GPU utilization on the same 96-H100 reservation is a powerful example of why orchestration matters. Before buying more GPUs, improve scheduling. Better routing and queue management can effectively create capacity from hardware you already own.
GPU utilization increased from ~20% to 43% on a reservation of 96 NVIDIA H100 GPUs, while cutting queue starvation by 74%, with blocked jobs falling from roughly 10 per day to around 4.
That’s the concrete result @SpreeAI saw after fixing their orchestration. When a unified diffusion model requires 80–100 GB of memory, you can’t simply throw workloads at a cluster and expect to use those GPUs efficiently.
SPREEAI was dealing with workload fragmentation, ad-hoc submissions, and storage I/O blocking that left expensive GPUs idle.
Working with Lambda’s ML engineering team, they implemented MLflow-based experiment orchestration with structured queuing and workload matching. They also connected Lambda’s Prometheus APIs to Grafana for real-time visibility into utilization gaps.
The video testimonial covers how they diagnosed the bottlenecks and what the remediation looked like.
AI compute is becoming a market, but markets need transparent pricing and measurable performance.
For inference, price alone is not enough: latency, reliability, model availability, and actual cost per useful response matter.
At ComputeFlux, we are building toward simpler multi-model access and more choice for developers.
Claims of up to 200x faster inference and 400x lower cost for classification are worth watching because agent economics are dominated by repeated small decisions. Not every step needs a frontier LLM. Routing simple decisions to specialized fast models could dramatically reduce agent cost.
Hot topic livestream: Learn about Jev
A buzzy new model Jev, by @typesafeai, reports up to 200x faster inference and 400x lower cost than comparable LLMs on classification tasks. It's aiming to solve the problem of slow and costly agent loops.
Next Tuesday, we’re bringing @sydneyrunkle from our engineering team at @LangChain, for a live convo on how TypeSafe AI’s model makes fast, structured decisions, where it fits in the agent loop, and how to build a harness with Jev.
Join here: events.langchain.com/webinar…
AI adoption is global. Data-center capacity is not.
That mismatch is exactly why compute needs a better market and routing layer.
Developers should not have to care where the GPU sits. They should care about whether the workload completes at the right quality, latency, privacy level, and cost.
Generative AI hit 53% adoption in three years.
Faster than the PC.
Faster than the internet.
But there's an access problem.
The US hosts 5,427 data centers. More than 10x any other country.
Demand is global.
The supply is concentrated.
io.net exists to change that.
Source: Stanford HAI, 2026 AI Index
270,000+ compute units running workloads on phones is another proof that AI compute does not have to live only inside hyperscale data centers.
The hard part is turning heterogeneous capacity into reliable inference developers can actually use.
That means routing, availability, verification, pricing, and a clean API layer.
Acurast is a verifiable compute network that runs workloads on smartphones instead of data centers. 270,000+ compute units onboarded so far.
Acurast just filed a B2 Token Transparency Filing with 2 gaps remaining.
$ACU live since January 20, 2026.
76% fewer prompt tokens. 15% lower inference cost.
This is why AI cost optimization cannot stop at choosing a cheaper model.
Compression + caching + routing + context management + provider choice all matter.
ComputeFlux is being built around the full path from request model compute completed task.
SOMA cut a prompt’s token count by 76% live, reducing the text you pay AI models to process.
On the latest Novelty Search, Subnet 114 presents its compression tool for coding agents, already in early access for Copilot and Codex.
The team reports ~15% lower inference costs on average across coding tasks. @say_gm_ has integrated SOMA’s open-source compressor, and Dendrite uses it in daily development.
Hosted by @const_reborn
Full episode in the first comment
EmbeddingGemma 2 is another sign that the AI stack is becoming specialized: small on-device models for private multimodal retrieval, larger remote models for reasoning and generation.
The future is not one model running everywhere. It is a heterogeneous stack that chooses the right model and compute location for each step. ComputeFlux is building orchestration for that world.
Reply ROUTE to test your workflow.
34M+ delivered compute hours shows that existing GPUs can become useful supply. The next problem is aggregating demand: developers do not want separate integrations for every provider, hardware type, model, and billing system.
ComputeFlux is building that demand interface-one compatible API, model/provider routing, and a path to decentralized capacity.
Beta access is open: test.computeflux.ai/activiti…
34M+ compute hours delivered.
Up from 33M in July.
No new data center required. Just GPUs that already existed, put to work.
@ionet lets you use what's built. And pay a fraction of hyperscaler prices while doing it.
Open models need more than open weights. They need reliable serving, portable APIs, routing, observability, and access to diverse compute. Otherwise "open" still ends at an infrastructure bottleneck.
ComputeFlux gives developers one OpenAI-compatible interface for trying and switching models without rebuilding the client each time.
Explore the free beta: test.computeflux.ai/activiti…
Open models need an open stack.
@ZyphraAI VP of AI Engineering Quentin Anthony shares how access to open software libraries and direct collaboration with AMD are helping the team train bigger models, keep growing compute resources working efficiently and pave new ground in AI.
Learn how Zyphra trained its ZAYA1-8B reasoning model from scratch on a full-stack AMD platform: bit.ly/4ixfdaB
Confidential inference should be verifiable, not a "trust us" privacy label. Attestation lets users verify that a workload ran inside a hardware-enforced enclave.
ComputeFlux's current beta focuses on multi-model access and routing through one compatible API. Our provider-network roadmap adds TEE-backed execution and verification for workloads where data exposure is unacceptable.
Building one? Reply TEE.
Jev 1.13 from TypeSafe is live on NEAR AI Cloud.
A System One decision model: send context and typed questions, get typed answers back instead of prose. Built for routing and classification. $0.042 per million input tokens, and output is free.
Get started: near.ai/models/typesafe/jev-…
Model routing is not 'if task == coding: use X'. A production router has to understand task shape, model capability, latency, cost, and observed outcome quality-then keep adapting as models change.
That is the direction of ComputeFlux: one OpenAI-compatible API, multiple models and providers, with routing built around the workload.
The beta is live and free to try: test.computeflux.ai/activiti…
New vid from @sydneyrunkle on how to build a model router into the harness:
1️⃣ Understand the tasks
2️⃣ Understand the models
3️⃣ Build the router in the harness
4️⃣ Track task outcomes
Agentic commerce is becoming tangible. An AI travel agent that can search, compare, book, and initiate a crypto payment illustrates a larger shift: software is moving from recommending actions to completing them.
That raises practical questions about authorization, spending limits, transaction receipts, dispute handling, and user control. Payment rails are only one layer of the system.
We think AI and Web3 will converge where programmable payments solve a real workflow-not where a token is added for its own sake.
Would you let an AI agent complete a purchase autonomously? Under what spending limit?
AI agents can now book your hotel and pay for it with crypto.
@travalacom is teaming up with @BNBCHAIN and @binance Pay to make it possible, with access to 2.2 million hotels worldwide.
Travala's Juan Otero joins @jennsanasie to explain how it works, why crypto rails are suited for AI payments, and what's next for agentic travel.
00:00 - Travala’s Juan Otero Joins Markets Outlook
01:51 - Travala’s AI Travel Agent
02:25 - How AI Agents Pay With BNB Chain’s Machine Payment Protocol
03:16 - Why BNB Chain and Binance Pay?
04:17 - Booking a Hotel by Chatting With an Agent
07:05 - Can You Trust an AI Agent With Your Wallet?
08:00 - Crypto Rewards vs. Credit Card Points
09:29 - Who Is Travala’s AI Agent For?
11:12 - The Future of Agentic Travel on Crypto Rails
The most interesting agent innovation may be happening around the model, not inside it.
Asynchronous subagents, persistent task state, scheduled actions, heartbeats, and recovery loops all turn a one-off prompt into a service that can do useful work over time.
But every one of those capabilities creates infrastructure questions: who executes the task, what happens after a failure, how are resources metered, and what permissions persist?
For an agent-native compute platform, these are core product questions rather than implementation details.
What is the hardest part of operating a long-running agent in your experience?
AI infrastructure has a demand-shape problem.
A startup may need a few GPUs for a weekend experiment. A research lab may need a burst of capacity for a deadline. A production agent may need modest but reliable inference around the clock. Those needs do not fit neatly into multi-year infrastructure commitments.
The next phase of AI compute should make smaller, variable workloads easier to serve-without hiding reliability or pricing trade-offs.
That is the problem space we're exploring at ComputeFlux: lowering the barrier between an idea and the compute needed to test it.
If you could change one thing about renting AI compute, what would it be?
The big AI question isn’t which model you can use.
It’s whether you can get the compute to build at all.
Trillions of dollars of data centers are in the pipeline. But they’re built mostly for buyers who sign 3-year commitments.
If you’re a researcher who needs 8 GPUs for three weeks, a startup validating a product, or a company without massive budgets, you’re locked out.
More capacity under the same access model just means the same gap, only bigger.
io.net is here to fix the access model, not just the supply.
Multimodal agents need more than a text model with an image feature bolted on. They must interpret screenshots, documents, dashboards, and changing interfaces while keeping their reasoning grounded in what they see.
NVIDIA's Nemotron 3.5 Super VL announcement highlights how quickly open models are expanding into those workflows. But deployment still comes down to memory requirements, throughput, reliability, and total cost per completed task.
We believe developers should be able to choose the right model and compute for each workload rather than commit to a single closed stack.
Which multimodal agent use case would you put into production first?
Nemotron 3.5 Super VL is the latest open model from @NVIDIA with vision built in natively (120B total, 12B active per token, 1M-token context, text + image + document + GUI + video input).
We benchmarked both deployment paths on Lambda with @sglang_proj:
* NVFP4 on 1× NVIDIA Blackwell GPU, 1,860 tok/s peak (C16), 587 tok/s/user (C1)
* BF16 on 2× NVIDIA Blackwell GPUs, 1,669 tok/s peak (C16), 530 tok/s/user (C1)
One NVIDIA Blackwell GPU at NVFP4 out-throughputs two NVIDIA Blackwell GPUs at BF16 on the same 8,192-in / 1,024-out workload, roughly 2.2× the per-GPU throughput, because Blackwell runs FP4 natively.
Both deployments run speculative decoding with multi-token prediction (EAGLE, 5 steps, 6 draft tokens).
Model card and full deployment instructions at lambda.ai/inference-models/n…
Private AI is becoming a systems-design problem, not just a model-selection problem.
This Phala example points toward an important architecture: keep sensitive inputs encrypted until they reach an attested execution environment, and minimize the number of services that ever see plaintext.
That matters for agents handling documents, credentials, or financial workflows. A privacy claim is much stronger when users can verify the execution boundary instead of merely trusting a policy statement.
The opportunity for decentralized compute is to make verifiable execution practical and accessible-not just technically possible. Where would you require confidential inference?
Private AI begins on-device.
Case study: how @solrouterai keeps its backend outside the plaintext path and opens encrypted prompts inside an attested Intel TDX enclave on Phala.
phala.com/posts/solrouter-pr…
