Running DeepSeek V3 (685B) requires 8×H100 GPUs which is about $14k/month. Most developers only need 15-25 tok/s. sllm lets you join a cohort of developers sharing a dedicated node. You reserve a spot with your card, and nobody is charged until the cohort fills. Prices start at $5/mo for smaller models.The LLMs are completely private (we don't log any traffic).The API is OpenAI-compatible (we run vLLM), so you just swap the base URL. Currently offering a few models.
FL score
out of 100
Verdict
high confidence
Competition
12
competitors found, emerging market, funded players
Trend
No signal yet
A platform enabling individual developers to share GPU nodes for large LLM inference with unlimited tokens via a cohort model, tackling high costs and complexity.
The pain
The gap
Build angle
Strengths
Questions about this idea?
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Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Addresses a real and expensive problem for developers but enters a crowded market with high build complexity and strong incumbents, limiting a solo builder's competitive advantage without a hyper-specific niche.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Strong market demand but faces significant competition and high operational complexity, making differentiation and pricing power difficult for a new entrant.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Addresses a clear problem for a reachable audience but the high complexity and the need for significant specialized expertise make it unsuitable for a solo creator aiming for simplicity and rapid iteration.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A clear value proposition for a specific audience but significant business model and technical assumptions, coupled with challenging distribution in a crowded market, make it high-risk for a micro-SaaS.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Addresses a desperate need for affordable large LLM access for specific users, with clear market demand, but faces challenges in validating the narrowest wedge and competing in a crowded space.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
RunPod offers a developer-centric GPU cloud with both secure data center compute and a community cloud leveraging peer-to-peer resources.
Pricing: Community Cloud RTX 3090 from $0.22/hr; Secure Cloud RTX 4090 from $0.34/hr; H100 PCIe priced at $1.99/hr.
SaladCloud operates a large distributed network of consumer GPUs, pooling idle capacity for affordable large-scale compute.
Pricing: GTX 1050 Ti from $0.015/hr; RTX 4090 starting at $0.16/hr; H100 NVL at $0.99/hr. Enterprise pricing for high-volume use available upon contact.
Vast.ai is a peer-to-peer GPU marketplace where individuals and data centers rent out unused capacity, with prices fluctuating based on supply and demand.
Pricing: RTX 3090 from $0.13/hr; RTX 4090 from $0.31/hr.
Akash Network uses a decentralized model to connect users directly with compute providers through a permissionless marketplace with an open bidding system.
Pricing: RTX 4090 around $0.40/hr; H100 GPUs from $1.20/hr.
Jarvis Labs offers on-demand access to the latest NVIDIA GPUs like H100 and A100 at competitive hourly rates with no minimum commitments.
Pricing: H100 SXM $2.69/hour; A100-80GB $1.49/hour; RTX5000 $0.39/hour.
FAR AI is a distributed AI inference network connecting consumer and enterprise GPUs into a single network, routing requests to optimal nodes and allowing GPU owners to earn income.
Pricing: Pricing not yet publicly available; node registrations are open with developer API access scheduled for Q2.
Hosted.ai provides a GPU-as-a-service software stack that optimizes the management and consumption of GPU resources through pooling, multi-tenant workload placement, and overcommitment.
Pricing: Pricing not publicly available; focuses on improving utilization for service providers.
CoreWeave is a GPU-native cloud provider purpose-built for training and inference at scale, optimized for modern AI stacks.
Pricing: H100 x 8 priced at $1.99/GPU/hour with a 12-month commitment; on-demand H100 at $3.39/hour.
Lambda Labs offers a GPU cloud platform tailored for AI developers and researchers, providing on-demand access to high-end GPUs.
Pricing: Pricing details not explicitly provided, but noted for 'research-friendly pricing with pre-configured ML frameworks'.
Inferless is a serverless GPU platform enabling developers to deploy machine learning models with a focus on cost-effective, high-performance AI inference.
Pricing: Their 'shared GPU pricing is not something I have seen anywhere' according to a user, but specific numbers are not public.
Agentical is a platform enabling users to share and rent their GPU computing power via a browser using WebGPU for AI chat or other tasks.
Pricing: Pricing not explicitly stated, but focuses on a community-driven approach to computing.
GPUStack is an open-source GPU cluster manager for running LLMs, allowing organizations to create unified clusters from various GPUs.
Pricing: Open-source, no direct pricing as it's a self-hosted solution.
What they charge
Recent news
Hacker News, April 04 2026
Tech Times, April 02 2026
Pulse 2.0, March 25 2026
Tech Funding News, April 01 2026
Seeking Alpha, March 05 2026
Market signals
The GPU for AI market is experiencing explosive growth, projected to reach $26.09 billion by 2032 from $3.34 billion in 2023. Significant funding rounds are common, with cloud GPU platforms raising $4.85 billion in equity funding across 12 rounds in 2025 (as of November). The market is driven by the increasing adoption of AI, machine learning, and data analytics across industries, with a strong emphasis on optimizing inference costs and GPU utilization.
What frustrates people
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI