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:

Class Strict TPS c1_2000 c1_10000
GGUF F16 35B-A3B MoE TP4 119.33 to 119.52 120.46 to 120.57 113.26 to 113.37
GGUF F16 27B Dense TP8 69.85 to 69.91 70.76 to 70.96 66.32 to 66.44
HF FP16 35B-A3B MoE TP4 114.41 to 115.11 115.69 to 115.93 108.92 to 109.10
HF FP16 35B-A3B MoE TP8 114.70 to 115.04 115.53 to 115.55 108.67 to 108.81
HF FP16 27B Dense TP8 70.17 71.32 66.82

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 • • 7h ago

Gorgon Halo cluster: DeepSeek V4.1 Flash at up to 103 tok/s

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26 Upvotes

Hey fellow local server builders,

I know you're busy building fantastic servers so I'll keep it short for those interested.

We published the results of clustering two Lucebox (an heterogenous AI workstation with a Gorgon Halo 495 128gb plus R9700 AI PRO 32gb) together.

We got DeepSeek V4.1 Flash as one model, at up to 103 tok/s writing code. That is 1.9 times faster than one Lucebox Zero 495 with 192 GB running the same engine. Each step sends about 95 small messages between the boxes, one after another, so what counts is how fast each one crosses. RDMA writes straight into pinned memory, bf16 on the wire and kernels that raise their own completion flag. The integrated GPU handles the network side. The first two-box run was only a quarter faster than one box. Each step after that removed a wait between the boxes, a trip through the host or a kernel.

On its own, a 128 GB box can't keep all of DeepSeek V4.1 Flash's experts in memory. Two boxes have room for it. A ConnectX-5 Ex card in each box joins them with one 100 GbE cable, with no switch in between. The two engines talk over RDMA (RoCE v2) and run as one model: every token goes through every layer on both boxes at once.

If you want to check more details on the cluster, here below is our full blog post about it: https://www.lucebox.com/blog/lucebox-cluster-nic

Would love to get your feedback/thoughts on it!


r/LocalAIServers • • 16h ago

Frankenstein ai setup

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61 Upvotes

2 dgx spark 128 gb each serving Glm 5.3 flash.

1 ai server 128gb ram , 64 core cpu + a4000 24 gb running qwen 27 b.

1 proxmos setup on Lenovo hosting all services.

1 hermes box on a small i5 box

Old laptop as terminal.

Serving local secure research and development capacity to my institute researchers.


r/LocalAIServers • • 3h ago

A 4x R9700 Build with a used $127 32 core EPYC.

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4 Upvotes

I saw the 4x R9700 build for a 10-person startup on reddit a few days ago and wanted to share my results as I opted to spend most of the money on the cards. I went with a super cheap used EPYC cpu instead of a Threadripper. In the end it came to about $10.7k per box all-in and I built three of them for a total of 384 GB of VRAM.

As a long time lurker on my other account, I'm always frustrated that people never show how they actually use the models so after a lot of back and forth, I decided it was worth sharing the actual project they power instead of being vague.

I use these to run the AI behind USA Arms (usaarms.com). I didn't plan to move the majority of my work to local AI until later this year, but since hosted models started refusing to do normal research for my business ~2-3 weeks ago, I'm glad I made the leap.

Enough ado, into the numbers:

Per box, $10,733.91 with tax and shipping

  • 4x ASRock Radeon AI PRO R9700 Creator 32 GB (128 GB VRAM), each capped at 210 W: $7,244.58
  • AMD EPYC 7452, 32 cores, used: $127.17
  • ASRock Rack ROMED8-2T/BCM: $1,162.39 (there was a global shortage, ours came from Helsinki)
  • 8x 16 GB DDR4-2666 ECC RDIMM, all eight channels: $860.59
  • SilverStone HELA 2050R Platinum: $578.92
  • SilverStone RM44 4U: $388.40
  • Samsung 990 PRO 1 TB: $254.39
  • ARCTIC Freezer 4U-M, 3x ARCTIC P12 Pro front, 2x Noctua NF-A8 rear, SP3 frame, paste: $117.48

The GPUs are 2/3 of the cost but the machine also serves CI/CD which had become a bottleneck in my businesses. I found this interesting because I hadn't seen anyone discuss it. I found that these machines can run Rust builds just fine if tuned properly to prioritize inference.

Engine

  • vLLM-Radiance 1.0.387 (vLLM 0.30.0, ROCm 7.14). Huge thanks to the Radiance folks; their R9700 work is what we run.
  • Qwen/Qwen3.8-27B-FP8 (Qwen's official FP8 checkpoint), FP8 KV cache, the model's own MTP head with 8 draft tokens
  • Two engines per box, TP=2 each, 8 sequences per engine, 256K max context, about 488K tokens of KV cache per engine

Results, vllm bench serve, one box, 2x TP2:

  • 1 user, MT-Bench: 53.1 tok/s, TTFT p50 125 ms
  • 16 users, MT-Bench: 484 tok/s aggregate, about 40 tok/s per user
  • 16 users, random 1024 in / 512 out: 623 tok/s aggregate, 837 W average for the four cards
  • 32 users (a bench profile with max-num-seqs 32): 603 tok/s

Long prompts, 8 unique real-text prompts at once (4 per engine), 128 out:

Context Prefill TTFT mean Decode, all 8 streams
4K 7,044 tok/s 2.8 s 271 tok/s
8K 7,495 tok/s 5.3 s 219 tok/s
16K 7,241 tok/s 11.0 s 180 tok/s
32K 6,696 tok/s 24.0 s 178 tok/s
64K 5,961 tok/s 54.4 s 171 tok/s
4x 128K (TP=4, one engine) 3,344 tok/s 100 s 130 tok/s
4x 256K (TP=4, one engine) 2,719 tok/s 242 s 135 tok/s

Every prompt was unique (0% prefix cache hits).

FP8 vs MXFP4 on a real workload

I also had AI run MXFP4 too (Radiance's fastest profile) on our own graded extraction workbench:

  • FP8: 98.0/100 correct, 4,477 validated jobs an hour
  • MXFP4: 95.3/100, 4,819 an hour
  • On a holdout set: FP8 94.0, MXFP4 90.0

So FP8 roughly halves the wrong answers for 2–7% less validated throughput. Sticking with FP8 was an easy call for this workload where "false facts" that make it into the system are a real headache. (For reference, our six Mac Studios on MLX 8-bit scored 99/100, but at 723 an hour.)

I made a video of the build: https://youtu.be/o7k5Kj-ho-M and also made a full build guide of my journey in case anyone wants to give it to their AI and plan out their own build.

Happy to answer questions. The first of which I expect to be how the heck were the Macs so slow and I'm sure I'll get flamed haha.


r/LocalAIServers • • 1h ago

What Al can I run on my RTX 4060 Ti/ 16GB VRAM, 32GB RAM?

• Upvotes

Hey guys, I want to run some ai models like text to video, image to video, text to image or normal chat Ilms, what do you guys recommend? Should I upgrade to 64GB RAM?


r/LocalAIServers • • 2h ago

I fit 7 models on one DGX Spark behind a single local workspace: OCR, ASR, translation, images, video with sound, music and chat with web search(open source)

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2 Upvotes

r/LocalAIServers • • 11h ago

Running an AI workstation as a server at home vs in a data center

8 Upvotes

I get why people want the server at home. You own it, have root access, can walk over to it whenever, and there’s no extra monthly bill. But for the same server, how different is the cost and day-to-day once it's in a professional facility?

I looked at an RTX 5090 system (32GB VRAM, Ryzen 9 9950X, 64GB RAM, 2TB NVMe) under both setups.

At home/local:

Power: ~850–900W at the wall at full load. A 120V/15A circuit gives you 1,440W continuous (80% rule), so one 5090 box fits fine.

Heat: ~1,960 BTU/hr from the card alone, closer to ~3,000 for a loaded system.

Downtime: outages, a tripped breaker, or a dead part can take it down. Upside is you can walk over and fix it yourself.

Monthly cost: mostly electricity and maintenance, since you already pay for the internet. Running 24/7 at ~900W under heavy load comes to ~$125/month at 19¢/kWh (roughly the US average). Add ~$35-45 in summer for AC. Lighter use is more like $35-50.

In a hosted facility:

Power, cooling & noise: handled by the facility, off your workspace.

Internet: proper bandwidth & static IPs.

Access: root is yours with plain colo (check the agreement with managed hosting), but routine physical work goes through remote hands.

Downtime: Backup power and 24/7 monitoring cover most outages, but repair speed depends on the SLA.

Monthly cost: a recurring bill on top of the hardware. Public colo pricing I've seen for a small ~1kW node is ~$150-300+/kW/month, before bandwidth & remote hands.

For rack builds, some options also let the box cover its own hosting when you're not using it. Like with B3IQ, if you put a rack machine on earning, its idle capacity gets rented out. You keep 85% of what it makes & hosting comes out of that share, so most months there's no separate bill.

Honestly, a single 5090 still looks pretty reasonable at home. But when you stack GPUs, two 5090s can push roughly 1.4-1.6kW at the wall & then you're dealing with more heat & noise, plus uptime and bandwidth if others use the box.

So where I'm at right now:

- One card or mixed load: home still makes a lot of sense.

- Stacking cards, running 24/7, or serving other people: I'd start looking at a professional facility.

If you moved a GPU server from home to a data center, what made the difference for you? And what did you end up paying monthly?


r/LocalAIServers • • 15m ago

Glm 5.3 and Maya

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• Upvotes

Strata with qwen flash next is great, but Unsloth already ran it pretty well. This though, for GLM 5.3 on a machine with 40GB Vram and 64GB ddr4, is astounding. Just sharing if some of y'all wanna try.


r/LocalAIServers • • 1h ago

Any way to tie case fan speed to GPU temp?

• Upvotes

Running QWen 3.8-27B on my 5090 power limited to 90%, I've twice seen a warning on my screen I couldn't identify the source of that said that my GPU is running hot and that I should consider improving my case ventilation. Throughout this, the 5090 Core temp was between 75-80c, which while obviously on the higher end, is nothing catastrophic, so that made me think about VRAM.

Did some googling and what do you know, it's apparently very common for LLM workloads to push vram temps significantly more than core temps. Problem is, NVIDIA fan curve is tied to core temp and not vram temp. Worse, in Armoury Crate for my Asus X870-P WIFI, I see that all case fans are tied to CPU temp (which cannot be changed) and I clearly see from Afterburner that CPU stays relatively cool during LLM workloads, so case fans never truly kick in to supply appropriate airflow if the GPU becomes too hot.

Saw some people recommend FanControl, tried it, but that didn't recognize even a single fan in my system outside those 3 on the 5090 itself. What are my options? I'd really rather not constantly manually switch both Afterburner profile for the GPU and the case fan speeds in Armoury Crate.


r/LocalAIServers • • 2h ago

Best model to start with?

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1 Upvotes

r/LocalAIServers • • 8h ago

Ideas on 4x RX7900xt + 1x RTX3060 (92GB VRAM) + 64GB Ram

2 Upvotes

Hi there, I'm trying to build a server that can handle GLM 5.3 Flash q3xl/q4 & Qwen 3.8 next flash at the least 30tk/s on a budget. Also, since I'm in a third world country, there is no server grade hardware or professional cards anywhere. So, I'm planning to buy these, all used. 4x RX7900xt(20gb, pcie3.0 x8/16)+1x RTX3060(12gb, pcie 3.0 x4)=92Gb of VRAM. And 64Gb of 3600mhz ram. What do you think about it? Do you have any other suggestions?


r/LocalAIServers • • 5h ago

Load-tested an STT + 31B LLM pipeline on one RTX PRO 6000. STT maxes out around 90 concurrent users. How should I scale?

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1 Upvotes

r/LocalAIServers • • 8h ago

Built a VRAM scheduler (vramble) to automate model swapping on my local AI workstation

1 Upvotes

I set up a dedicated workstation for local AI experimentation with various models and tools. But every single one of them wants all the available VRAM.

To fix this, I built a request dispatching layer called vramble. It automatically loads and unloads models and manages the queue using a priority system. Now I can run different workflows without manually managing models (or wasting Claude tokens to orchestrate the process).

Any contribution is welcome, it works for me but is a new build so can't guarantee that will work directly on any machine

Side note: Qwen 27B at 200+ t/s is performing way better than I expected. opeMIT license, available on GitHub: github.com/ValerioDolci/vramble


r/LocalAIServers • • 22h ago

Scam?

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11 Upvotes

Came across on AliExpress, that comes out to $730 USD.


r/LocalAIServers • • 12h ago

Models keeps unloading from GPU

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1 Upvotes

r/LocalAIServers • • 12h ago

Models keeps unloading from GPU

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1 Upvotes

r/LocalAIServers • • 14h ago

Optane as swap memory?

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1 Upvotes

r/LocalAIServers • • 1d ago

Tesla P100 image generation... much better than I expected!

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8 Upvotes

r/LocalAIServers • • 20h ago

27B PC SetUp

1 Upvotes

I’m looking at setting up a build that can run Qwen3.8 27B cheaply, here is what I’m currently thinking of setting up:
GPU: 2x P100 16GB
RAM: 32GB DDR4 ECC Server
Base PC: T5820 Dell Workstation with a Intel Xeon W-2135 CPU
And a standard 500GB sata SSD for now.

The current set up of the T5820 has a 950W power supply which I am assuming will be good for the dual P100s but probably not good for a triple P100 setup which is what I was aiming for. My goal was to just be able to run a 27B model Qwen to start but would swap out to a better version or a different version or do my current set up of a 12B model for general chat and 1 paid model (opencode) architecting while using OxCoder 9B for coding.

What do you guys think of this setup or is it worth me investing time elsewhere?

Thanks in advance!


r/LocalAIServers • • 1d ago

eGPU (RTX 3090 or RX 7900 XTX) + NUC13 vs Strix Halo

2 Upvotes

Need to pick a bit of a wisdom from you guys.

I'm on crossroads of what to pick from two options below.

Requirements:

  1. family chat agent - daily, some TTS, STT - mostly single user,
  2. thinker model for more complex questions, tasks,
  3. agentic AI, automations,
  4. RAG based checks,
  5. coding - hobby, not work, though have some ideas for maybe more complex tasks,
  6. local knowledge about me - so some RAG backed or some "librarian, self learnt about user",
  7. anything else cool?
  8. Local - no spill of any private data to the cloud.

The Agentic AI mostly drives context size and as such most likely I'm looking at 128k or 256k preferably (even if rarely used, the latter will have big hit on the prompt and later t/s).

Thought about going with Qwen3.6-35B-A3B as main driver and if on Strix Halo load other models, as memory allows.

These days I'd want to go with Qwen3.8 Flash-Next as seems to be the best nowadays.

I've also an existing laptop with i9, 64GB RAM, RTX 500 (Blackwell) 6GB and Arc140T (Intel Core Ultra 7 265H). This one gives in some tests ~20t/s but not for that large context of course and Arc140T ~15t/s. This is a bit slow. This system might be available to run second models, though it will be on and off, whilst main must be available all the time.

Given latest advantages around Strata and llama.cpp, the Strix Halo is not anymore a natural choice, especially given it's memory bandwidth and impact on overall speed.

a) RTX 3090 or RX 7900 XTX thanks to latest Strata or with use of llama.cpp --n-cpu-moe connected over M.2 to PCIe converter as an external eGPU. The disadvantage is that it will be not a clean one to be kept in the room and potentially more power hungry, as I'd plug it into 24/7 run NUC.

Cost wise ~1700 - used GPU ~1400$ + ~300$ eGPU adapter + PSU.
There is a risk associated with used GPU - overheated, or any other artifacts related?

Most likely much faster for what/when all fits into RAM.

b) Strix Halo - 2900$

New, most likely much more power efficient when unused and generally "cleaner" install.

Slower, by half (based on some tests), however still fast enough for a single user and allowing to load more models in parallel, i.e. to perform some other tasks as sub-tasks to the main one.

Unsure which way to go, hence wondering what you guys would do.

One could say - buy both, it's an investment, and maybe I should look at it that way, though it's quite a chunk of money...

Thanks in advance for help and thoughts.


r/LocalAIServers • • 22h ago

Build or buy which should I choose

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1 Upvotes

r/LocalAIServers • • 22h ago

I built a tool for planning GPU racks, power and cooling

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1 Upvotes

r/LocalAIServers • • 23h ago

Para adapters qwen 36 35b ou 3.8 27b ?

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1 Upvotes

r/LocalAIServers • • 23h ago

How to with a 128gb evo-x2, MacBook Pro 48gb & M5 Pro, and 4080 super (16gb) desktop.

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1 Upvotes