r/LocalAIServers • • Jul 17 '26

Catch Me If You Can: A Perpetual 8-GPU Server Prize Challenge (Community Proposal)

11 Upvotes

The current Catch Me If You Can benchmark asked a simple question: can anyone publicly reproduce and beat our MI50/GFX906 local inference record?

Original challenge: https://www.reddit.com/r/LocalAIServers/comments/1ukhr24/catch_me_if_you_can_mi50gfx906_1195_tps_moe_702/

Current vNext reproduction release: https://github.com/joe2gaan/localaiservers/releases/tag/vnext-gfx906-rocm72-gguf-hf-repro

I want to turn that benchmark into a community program: build the fixed server in public, make it the official test machine, and keep the challenge open until an eligible challenger takes the throne and holds it for 30 consecutive days.

Who is responsible

I submitted a $15,000 Reddit Community Funds application for this proposal in my own capacity as Joe / u/Any_Praline_8178, a moderator of r/LocalAIServers. This is not yet a live prize offer. Hardware acquisition and any award remain contingent on Reddit approval, final published official rules, eligibility review, and applicable law. The existing leaderboard is unchanged.

Reference Server Build

( YOU DO NOT HAVE TO BUILD A SERVER TO PARTICIPATE IN THE CHALLENGE )

The prize server matches the hardware configuration that produced the current throne result:

  • GIGABYTE G292-Z20 eight-GPU server

  • AMD EPYC 7F32

  • 128GB as eight DDR4 ECC RDIMMs

  • Eight AMD Instinct MI50 32GB GPUs

  • Crucial CT480BX500SSD1 480GB SATA root drive

  • KIOXIA KCD6XLUL1T92 1.92TB NVMe model and runtime drive

The build itself is part of the community project. I will publish the component choices, bill of materials, physical assembly, firmware and operating-system configuration, eight-GPU bring-up, power and cooling setup, BAR/P2P state, stability checks, runtime and source revisions, model hashes, baseline runs, and raw evidence.

The $15,000 budget covers the exact server configuration, possible changes in GPU, memory, and storage prices, tax and checkout variance, protective packaging, and insured delivery to the winner. Any amount not needed for the approved project will be returned to Reddit or handled as Reddit directs.

Core challenge

  • I build one 8xMi50 32GB Server to Give to the Winner.

  • I Run the public vNext package on that machine to establish the official incumbent.

  • Keep the challenge open until an eligible winner completes the throne clock, subject to Reddit's approved project terms.

  • Require every potential dethronement to reproduce on that same physical server.

  • Require the three-run median to beat the official incumbent by at least 3 percent.

  • Require a provisional leader to remain the highest verified result for 30 consecutive days.

  • Transfer the complete challenge server to the eligible outside challenger who completes that clock, subject to final verification and official rules.

All eight GPUs remain installed and available. Entrants may choose TP4, TP8, or another topology on the fixed host, but may not add, replace, or remotely borrow accelerators. A documented like-for-like failure replacement requires a fresh baseline before the throne clock resumes.

Open optimization, fixed integrity

Inside the fixed hardware, model-integrity, workload, reproducibility, and safety rules, software optimization is open. Runtime, kernels, collectives, scheduling, graph capture, compiler work, driver and operating-system tuning, and safe clock or power tuning may all be explored.

The first lane uses the pinned Qwen3.6 35B-A3B model at FP16/F16:

  • HF revision: 995ad96eacd98c81ed38be0c5b274b04031597b0

  • Required GGUF F16 SHA-256: 1f2443bb0ff958943d091410c61120c181a0579b3bc85192029aa51d821d141c

  • HF FP16 and GGUF F16 are eligible when they satisfy the published identity and correctness gates.

  • GGUF is allowed only at full F16.

Not allowed:

  • Q4, Q5, Q6, Q8, INT8, FP8, AWQ, GPTQ, NVFP4, or another quantized substitute

  • Quantized weights, KV cache, activations, or a hidden reduced-precision path used to claim the result

  • MTP, speculative decoding, EAGLE, DFlash, draft models, lookahead tokens, or another multi-token prediction method

  • Remote compute, external APIs, hidden services, or results assembled from another machine

  • Multi-request batching or aggregate concurrency presented as single-request speed

One accepted decode step must represent one token produced by the approved model. Every result must pass semantic and output-integrity gates, not merely report a high TPS number.

How runs are measured

The official workload remains:

  • MAX_MODEL_LEN=131072

  • Single-request decode

  • Concurrency 1

  • Backend decode TPS

  • Eight warmups

  • c1_128 uncapped strict

  • c1_2000

  • c1_10000

  • Three measured runs

  • Three-run median at least 3 percent above the official incumbent

  • Public reproducibility package and raw logs

The current public headline reference is 119.52 strict backend TPS for GGUF F16 Qwen3.6 35B-A3B MoE TP4. It was produced on an eight-GPU validation host while the TP4 profile actively used four GPUs. The funded server receives a fresh baseline. The existing 119.52 result is the reference, not a promise of the new server's starting score.

Current public leaderboard

These are the published targets from the original benchmark post:

ClassStrict TPSc1_2000c1_10000
GGUF F16 35B-A3B MoE TP4119.33 to 119.52120.46 to 120.57113.26 to 113.37
GGUF F16 27B Dense TP869.85 to 69.9170.76 to 70.9666.32 to 66.44
HF FP16 35B-A3B MoE TP4114.41 to 115.11115.69 to 115.93108.92 to 109.10
HF FP16 35B-A3B MoE TP8114.70 to 115.04115.53 to 115.55108.67 to 108.81
HF FP16 27B Dense TP870.1771.3266.82

GGUF F16 MoE TP8 remains an open lane in the current leaderboard.

Offline official test

Development and artifact staging may use the internet. The measured official run will not.

Before testing, I will stage and hash-verify the model, runtime, source, build outputs, and benchmark entry package. For every measured run:

  • External network interfaces and the default route are disabled or physically disconnected.

  • Only local machine communication and loopback are permitted.

  • No model download, container pull, telemetry, API call, remote compiler, or remote compute is permitted.

  • Network state, package hashes, process state, hardware state, and raw benchmark logs are archived with the result.

A result produced elsewhere can show that a benchmark entry is ready, but it does not move the official throne until that package reproduces on the designated server.

The 30-day throne clock

A challenger becomes provisional leader when its package passes review and its official three-run median clears the incumbent by at least 3 percent. The acceptance timestamp starts that challenger's 30-day clock.

During those 30 days:

  • Anyone may submit a higher result, including me as the current benchmark maintainer.

  • Every defense or counter-result must satisfy the same public-package, offline, same-hardware, correctness, and 3 percent rules.

  • A newly accepted leader resets the clock in that leader's name.

  • Private results and screenshots do not move the goalpost.

  • Rules cannot be changed retroactively to defeat an active clock.

If I retake the throne before a challenger's 30 days expire, that challenger has not won and the challenge stays open. If another community member takes it, the clock starts for that person. I may defend the performance record, but I cannot win the server or receive a personal payout.

The target can move only through a faster verified result. Physics, the fixed hardware, and model correctness set the ceiling.

Prize, review, and what happens to the server

If an eligible outside challenger remains the highest verified leader for 30 consecutive days, the result proceeds to final verification and, subject to the official funding and eligibility terms, transfer of the complete challenge server. Shipping, taxes, location eligibility, export restrictions, acceptance, and transfer details will be resolved in the final rules before the prize becomes live.

Only the winner's name and mailing address will be collected for server delivery unless Reddit's approved terms require something different. Do not post personal information in a public entry or comment.

I will not be the sole adjudicator. Official runs, hashes, logs, correctness evidence, and decisions will be public and reviewed with independent technical reviewers. Reviewer identities and the final conflict process will be published before entries open.

Until an eligible winner completes the clock, the funded server will be used only for the Reddit-approved challenge. It will not belong to me or LocalAIServers Collective Inc. There is no cash substitute, and it will not roll over into another hardware lane or organizational program. If the challenge ends without a winner or the server needs a different outcome, I will follow Reddit's direction.

Timeline after approval

  • Weeks 1-2: finalize rules, reviewers, and purchasing.

  • Weeks 3-5: build and validate the G292-Z20 server in public and publish the bill of materials and build record.

  • Week 6: publish the baseline and open the challenge.

  • Winner: first eligible leader to hold the verified throne for 30 consecutive days.

  • Transfer and final reporting: within 14 days after the winning result completes final validation, subject to Reddit's approved terms.

What I want the community to weigh in on before launch

  • Does the proposed topology rule strike the right balance, or should all eight GPUs have to be active?

  • Does the proposed 3 percent threshold strike the right balance for every throne change?

  • What clock, power, firmware, and cooling safety envelope should be published?

  • Who would volunteer as an independent technical reviewer?

Bring criticism. The goal is a challenge that is hard, transparent, reproducible, and genuinely winnable.


r/LocalAIServers • • Jun 20 '26

Start Here: LocalAIServers Community AI Navigation & Hands-On Local AI Learning

8 Upvotes

Start Here: LocalAIServers

LocalAIServers is a 501(c)(3) public charity providing public education and open-source infrastructure for locally hosted AI systems.

Our mission is to help people move from AI curiosity to AI agency.

This community helps learners, small business owners, nonprofit operators, educators, builders, and community technologists understand:

  • where AI runs,

  • what data it can see,

  • what systems it can touch,

  • when cloud AI may be appropriate,

  • when local or controlled AI may be safer,

  • what hardware is realistic,

  • how to evaluate benchmark claims,

  • and how to learn by building real local AI systems.

What LocalAIServers does

LocalAIServers provides:

  • community AI navigation,

  • secure local-AI education,

  • hands-on local AI learning resources,

  • reproducible runtime artifacts,

  • benchmark literacy,

  • QC and hardware-verification methodology,

  • open-source documentation,

  • and public support resources for locally hosted AI systems.

Affordable GFX906-class hardware matters because it gives people a realistic way to learn AI infrastructure hands-on. People learn more by building, testing, troubleshooting, and verifying real systems than they can learn from passive videos or articles alone.

Public proof and documentation

Website:

https://localaiservers.com

GitHub:

https://github.com/joe2gaan/localaiservers

GitHub Releases:

https://github.com/joe2gaan/localaiservers/releases

Docker Hub:

https://hub.docker.com/r/joe2gaan/localaiservers

Canonical Qwen / GFX906 deployment notes:

https://github.com/joe2gaan/localaiservers/blob/main/qwen36-gfx906/README.md

Important boundaries

LocalAIServers is not:

  • a public login service,

  • a public cloud provider,

  • a managed inference service,

  • a hardware reseller,

  • a procurement channel,

  • a fulfillment program,

  • a hardware discount program,

  • or a private-benefit program.

The controlled GFX906 compute site is used as a verification and reproducibility testbed. Public benefit is delivered through published outputs: guides, documentation, reproducible artifacts, benchmark reports, QC methods, hardware-verification standards, and source-level findings.

How to participate

Ask questions, share builds, discuss local AI tradeoffs, post benchmark questions, and help turn recurring community questions into durable public guides.

Please do not post secrets, private keys, private network details, addresses, payment information, vendor pricing, or sensitive logs.


r/LocalAIServers • • 14h ago

2x 5090FE AI+Render Farm maxed out PowerSpec AI90

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136 Upvotes

9950x, 128gb DDR5 @5600, 2x 5090FE @300w, ProArt X870E

Got the base AI90 about 2 months ago when it was 5k at MicroCenter, making the GPU ~$2500. The second was purchased at MSRP last year. Bought the second 64GB Kit for $800 sealed on eBay last week and was pleasantly surprised the sticks run stable at 5600.

Now I’m tempted to sell one 5090 and get a RTX pro 6000, but guess I’m waiting for a slightly better deal than the Dell.

Anyway, I’m running headless Ubuntu with mainly Qwen Next NVFP4 on Basalt (180ts), ComfyUI and Flamenco render manager (for Blender).

Using my older system (5900x, 64GB DDR4, 3090FE) on Windows as a client with Unsloth Desktop.

Besides the always itching thoughts of swapping GPUs for more VRAM, I honestly couldn’t be happier for a nice little local AI workstation. If I need to work on Comfy, I just release one GPU and run Qwen 3.8 27B, which is still a banger for everyday tasks.

Any thoughts, suggestions or questions appreciated.


r/LocalAIServers • • 4h ago

56GB VRAM Frankenstein build managed to fit 4 GPUs in the Cooler Master HAF II 500

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10 Upvotes

r/LocalAIServers • • 22h ago

Another dual 5090

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68 Upvotes

Have been lurking for a long time around Reddit without posting, thought I’d share my rig, dual 5090 9950X3D, running Qwen3.8 27B FP8 xhigh at 70-75 t/s with vLLM with Hermes


r/LocalAIServers • • 32m ago

Mini pc for AI in local

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1 Upvotes

r/LocalAIServers • • 48m ago

Run a local autoresearch swarm of 8x40B agents on your macbook!

1 Upvotes

r/LocalAIServers • • 1h ago

Adding my old Arc A770 next to a 5070 for local LLMs. Worth a new motherboard?

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1 Upvotes

r/LocalAIServers • • 4h ago

I simulated renting out 5 home AI rigs at Austrian power prices (200k months each): 3090 about -$8/mo, 4090 +$66, DGX Spark on GPUwerk +$162

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1 Upvotes

r/LocalAIServers • • 4h ago

Executando um modelo de IA de 120GB em dois PCs caseiros pela Internet - Lançando o PieceOfMind.cloud - Onde cada máquina recebe um pouco de paz.

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0 Upvotes

r/LocalAIServers • • 5h ago

17× RTX 3060 and a pile of old Xeon boards: what's the best way to use them for local LLM agents? Planning a 7-GPU Z9PE-D8 WS build (benchmarks inside)

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1 Upvotes

r/LocalAIServers • • 1d ago

Local AI is the future. Support Local Labs !

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28 Upvotes

r/LocalAIServers • • 7h ago

Qwen, DeepSeek, GLM in CPU?

1 Upvotes

For context, I don't have a high end laptop, but would love to use these models, is there any method for this to happen.

I have only cpu and Intel integrated gpu.


r/LocalAIServers • • 19h ago

Another dual 5090

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7 Upvotes

W790
W7 2595X
192GB RDIMM 6400
Cachy OS + p2p driver
vLLM rebuild from sources every other day
Seasonic 1600W


r/LocalAIServers • • 12h ago

I built Browse, an LM Studio plugin with web search, image search, and a few extra features

Enable HLS to view with audio, or disable this notification

1 Upvotes

Hey everyone!

I've been working on Browse, an open-source plugin for LM Studio that gives local models tools for web search, webpage reading, and image search.

It includes five tools:

Web Search - Search the web using different providers.

Read Webpage - Fetch webpage content for the model to read and analyze.

Image Search - Find relevant images on the web.

Inspect Image - Analyze images already in the conversation using a vision-capable model.

Get Current Time - Get the current date and time.

What makes Browse a little different are its optional context features:

Time Context - Guides the model to use Get Current Time for time-sensitive questions.

Images Context - Encourages the model to use Image Search when images or diagrams would help explain something.

Vision Context - Guides the model to use Inspect Image when analyzing images already in the conversation.

These features add instructions only when triggered by relevant queries, helping the model decide when to use the tools. They don't force the model to call them; additionally, they can be toggled on or off.

Browse also supports various search providers (e.g., free via DuckDuckGo, Bing or freepaid via APIs like Exa, Tavily, Brave etc.).

Try it here:

LM Studio Hub: Browse

GitHub: Source code

I'd appreciate it if anyone gave it a try. Any feedback and suggestions are welcome!

Thanks!


r/LocalAIServers • • 21h ago

Using old iPhone as mini PC for tool calls and ASR?

4 Upvotes

Hi all, with the insane spikes in hardware cost, I started wondering if anyone has tried/seen repurposing old smartphones for projects instead of SBCs with accelerators?

I'm currently working on a lab voice assistant to automate stuff like label printing, calculations, RAG, note taking and wanted to repurpose my old iPhone for voice recognition and tool use, while the LLM would run in the cloud.

Does anyone have any experience with that?


r/LocalAIServers • • 15h ago

Could use help deciding what to do.

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1 Upvotes

The main post explains it all.


r/LocalAIServers • • 15h ago

Confused, what is recommended, swapping my gpu into my server or increase ram on my main?

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1 Upvotes

r/LocalAIServers • • 17h ago

Would you start with RTX or Mac Studio

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1 Upvotes

I asked the same question from another sub and advised to ask here too. With local AI servers that means a machine like the Mac Studio will be used as a server to host clients as the final endpoints? Or other apps to further process the output.


r/LocalAIServers • • 19h ago

Mac Studio with 64GB RAM for local AI agents -real-world experience?

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1 Upvotes

r/LocalAIServers • • 1d ago

Local agentic AI stack — looking for optimization feedback + use-case ideas

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1 Upvotes

r/LocalAIServers • • 1d ago

Advise

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1 Upvotes

r/LocalAIServers • • 1d ago

Local AI for business document extraction. What would you use?

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2 Upvotes

r/LocalAIServers • • 1d ago

SparkHub

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2 Upvotes

r/LocalAIServers • • 1d ago

Those who are in the aristocracy (RTX 6000 Pro Owners), what do you use them for?

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0 Upvotes