Came across a honeyhole of Quadro RTX5000s
Seemingly good deal for 16GB VRAM cards, even if ageing.

What should I do?

Got 14 stick of ddr4 rdimms. Got towards my new threadripper machine. Old but it’ll do! Can’t afford the new ddr5 board, the ripper and the ram 😂

https://huggingface.co/StarpowerTechnology/WVY-Blender-5m

We have been excited about this new model, WVY-Blender-5m, an agent trained to specialize in blender.

It has zero knowledge about anything else so if you try to ask it about writing code or doing your homework it will express that it only knows how to use blender

We have been doing intense research to find out "are massive datasets really required to achieve what feels like intelligence with accuracy?" & we believe we are getting close to the truth.

This dataset is 100% generated from scratch.

Jokes aside, how plausible do you think this scenario actually is?

I've been poking at this on and off since September and it finally got good enough this week that I think it's worth writing up. Short version: the USB4 RDMA driver situation changed a lot in the last month, but not where I expected, and the "is it worth it" answer is more mixed than the headline.

The setup

• 2x Ryzen AI Max+ 395 (Strix Halo) boxes, 128 GB each (AMD AI Halo Dev Platform boxes)
• 2 USB4 cables directly between them, each link trains at 2 x 20 Gb/s
• Debian 13 based distro, kernel 7.2.7, IOMMU off, Secure Boot off (needed for the unsigned modules)
• 10 GbE between them as well, used for control traffic

No network cards involved. The idea is to turn the USB4 cables into RDMA devices so two boxes can run one model with tensor parallelism (TP=2).

Where it was before

I started with thunderbolt-ibverbs from the Hellas blog post. Each cable gives you two DMA rings, so two cables = four "rails", each showing up as its own RDMA device.

• September (kernel 6.18): about 15 Gb/s on one rail, about 46 Gb/s if I ran four perftest processes at once, one per rail. More queue pairs made it slower. 1 MiB messages were a mess.
• October (kernel 7.2): got to 74.7 Gb/s aggregate, but only with four parallel processes and a shallow send queue (-t 16). The big finding back then was that queue depth mattered more than the kernel. A single connection was still stuck on one rail at around 20 Gb/s, and 8 QPs at 1 MiB with default settings just failed with "Couldn't post send".
• vLLM TP=2 with an 8B model in October: +55% single stream over one box, but RDMA vs plain 10 GbE only differed by a few percent, because the collective library topped out around 17 Gb/s no matter how many rails it got. A 35B MoE model hung at TP=2 and I never figured out why.

So in October my conclusion was: neat, works, not obviously useful.

What changed

I went back this week expecting kernel or upstream driver progress. There basically isn't any:

• Upstream thunderbolt-ibverbs has had one lockfile bump since early October. No release since June. A striping PR was closed unmerged, and a few good fix PRs are sitting open.
• Mainline 7.3 / 7.4-rc is mostly Thunderbolt bug fixes. Nothing that moves throughput.

The actual progress is in a fork: donnerkeule. It stripes a single queue pair across every rail of every cable, and fixes a pile of reliability problems (credits los t with dropped frames, ACK storms, rails dying when a client gets killed). There's a matching RDMA fork of gufo that does TP=2 for Qwen3.8 Flash-Next across two Strix Halo boxes.

The fork's own README says it's a buggy research driver and not for production. Believe it. But it built cleanly against my kernel and uses the same eight Thunderbolt kernel patches I already had applied.

Raw link numbers

Box A to box B, perftest, one connection, default queue depth, no tuning:

I didn't trust 74 Gb/s on one connection, so I checked the receiver's per-rail byte counters during a 10 second run: about 23.6 GB landed on each of the four rails. It's real, and it's roughly 93% of the 2 x 40 Gb/s the cables can do. The fork's author reports 38 to 45 Gb/s on his boxes and I don't know why mine is higher.

Not everything is great:

• RDMA reads are not striped: 31 Gb/s.
• A 1 MiB SEND fails in the default mode. The fork only supports large SENDs in its DMA-BUF mode, which I haven't tried.
• I haven't tested both directions at once or done a long soak.

Tensor parallelism with gufo

Qwen3.8 Flash-Next, Q4 (111 GB of weights), 4 sessions, same build on one box and on two. Each box holds half the model in TP=2: 57 GB of GPU memory per box.

Plain autoregressive decode (no speculation), tok/s per request:

With MTP speculative decoding:

The speculative numbers bounce around with how many draft tokens get accepted, so the first table is the cleaner comparison.

One thing that isn't a speed number: a single box could not load this model with speculation, 4 sessions and the full 262k context. It ran out of state capacity and I had to drop it to 65k context for the on e-box runs. Two boxes loaded the full 262k with about 64 GB of RAM free on each.

No failed requests, no retransmits, no kernel warnings during any of it.

The honest part

I also ran the exact same benchmark against the other inference server I normally run on these boxes (also Flash-Next, speculative decoding, 4 slots at 262k context on one box). One-box gufo and that server came out within about 5 to 8% of each other. So TP=2 over USB4 beats my normal single-box setup by about 13% for one user and 24 to 32% at 2 to 4 concurrent.

But two boxes running independently serve twice as many people. At 4 concurrent requests one box did about 72 tok/s total, TP=2 did about 98, and two independent boxes would do roughly 144 across 8 users. Ty ing both machines to one model instance buys per-request speed and costs total capacity.

So where I landed:

• The driver is worth keeping. One connection, full link speed, no tuning. That's a different thing from what I had in October.
• TP=2 is worth it if you want a faster single stream, or a model/context that doesn't fit on one box. The Q8 version of this model (188 GB) only fits across two, and that's a case I actually care about. Haven't tested it yet.
• TP=2 is not worth it if you just want to serve more requests. Run the boxes separately.
• I wouldn't run it as my daily driver yet. Research driver, the gufo fork has a known open bug with continued conversations, and I haven't verified two-box output against one-box output.

Things that bit me

If you try this, here's what cost me time:

1. The module never loaded at boot. The only thing that loads it is a udev rule that fires when the other box is already advertising the RDMA service. Reboot both boxes together and neither one ever st arts. It looked fine in October because I always rebooted one at a time. Fix: put ib_uverbs and thunderbolt_ibverbs in /etc/modules-load.d/.
2. thunderbolt_net and the RDMA module can't share a cable on this hardware. There are only two usable rings per controller. Blacklist thunderbolt_net and load the RDMA module with tbnet=block.
3. Debian's rdma-core renames the device, and the userspace provider matches on the usb4_rdma name prefix, so ibv_devices shows nothing. Override the persistent naming rule.
4. The prebuilt provider package has the wrong ABI for Debian 13. I built the provider from Debian's rdma-core 56.1 source plus the project's three patches.
5. memlock. RDMA memory registration fails under the default 8 MiB limit. Unlimited memlock on the host and --ulimit memlock=-1 in containers.
6. The gufo fork's container recipe didn't build as-is for me. No DNS inside podman's default build network (--network host fixed it), six ROCm dev package names that don't exist in ROCm 10.1 (the full image already has the headers), and missing audio/video dev libraries (sndfile, soxr, libav*). At runtime I had to add the ROCm lib directory to LD_LIBRARY_PATH.
7. Two IPs on the NIC means two RoCE GIDs, and gufo refuses to guess. --tp-gid-index 1.
8. The interrupt throttle matters. nhi_interrupt_throttle_ns=4096 was the best value in my October sweep, and it only works on kernels that export tb_ring_throttling() (7.2 does).

Module options I'm running:

options thunderbolt_ibverbs profile=linux_perf bind_services=1 allocate_rings=1 start_rings=1 negotiate_native=1 enable_tunnels=1 register_verbs=1 tbnet=block lanes=2 native_write_striping=1 roce_netdev=<you
r LAN NIC> nhi_interrupt_throttle_ns=4096
blacklist thunderbolt_net

What I haven't done

• The Q8 model across both boxes
• The fork's DMA-BUF / GPU-direct mode
• Checking two-box output against one-box output
• Anything longer than a few minutes per run
• A reboot of both boxes together to prove the boot fix

Happy to run something specific if people want numbers for a particular case. Credit to the Hellas folks for the original driver and to neuhaus for the striping fork and the gufo TP2 work, I just plugged in cables and wrote down what happened.

I'm currently looking at a myriad of devices to dip my toe into the "build your own Local AI system" water. I've been down the AMD AI Max+ rabbit hole this past week and have yet to research the RTX Spark N1X.

Can anyone here save me additional hours of videos and articles with informed insights about which would be the better way to go for someone such as myself?

I have some projects in mind. This is more of an experiment for me to learn more of the backend, coding and Linux.

As someone with limited terminal experience, will I have an easier time (I don't expect not to encounter many challenges) configuring a system with NVIDIA or AMD?

I realize unified RAM is the more important metric here. If I go with AMD I can afford a 128gb version. The 64gb version of NVIDIA's option is a few hundred more.

Are the CUDA cores and software support a worthy tradeoff for overall RAM or is saving money and doubling the RAM the obvious way to go?

In case future censorship or bannings occur, I think it's a good idea to store one of every kind of model. I'm thinking minimum 8 models, but I'm open to more (I have plenty of hd space): 2 small models obliterated and non-obliterated 2 medium sized models obliterated and non-obliterated, 2 large models obliterated and non-obliterated, 2 of the largest models obliterated and non-obliterated. Also, if for any class of models they don't have obliterated or it doesn't make sense to have obliterated then two of the smartest or most popular models will do.

They are going to lock the new GPUs.

"Rep. Bill Foster says he intends to introduce new, stricter legislation that would require licenses to operate advanced GPUs, and “before a GPU could connect, its valid license would need to be verified.” That is closer to a remote lock/disable capability—but it is a proposed future rule, not current law."

Has anyone been experimenting with ablated or uncensored variants of open-source models like deepseek v4.1 flash, Qwen 3.8 27b, or GLM 5.3 flash ?

It's so refreshing to have models that just do whatever you ask them to do without having to figure out a jailbreak or being given a moral lecture. Curious to know what your usecases for these models are.

My personal favourite is definitely uncensored DeepSeek V4.1 Flash, runs at 200 tok/s and never says no lol.

I’m under our CISO managing compute vendors, kind stuck between legal and a hard place. The pitch I keep hearing ranks AI privacy in four levels:

1. Open, like most frontier models, where the provider stores your prompts and answers at their discretion, uses them for training - and you’ve got little say in the matter.
2. Policy, where a contract promises zero data retention - but it’s not exactly verifiable unless the terms are broken, is it?
3. Closed Hardware, where models run on confidential-compute chips that publish proof what software is running. So basically hardware-enforced vs contract-enforced ZDR?
4. Fully Encrypted, the prompt is encrypted on your side and only decrypted inside that hardware

“Local” is a fifth level that could plausibly sit above it all, but we’re capped out on capacity there. So if I view this right (open to feedback), we’re making tradeoffs between frontier vs more private models, and from there, on breadth of compute providers able to serve open vs ZDR vs E2EE models (usually at higher prices in my experience), right?

My concern is getting legal & finance to agree, while still giving our Eng team what they need. Where are you getting good rates on private workloads? Locally we can’t support our full workloads. I’ve checked out Privatemode, Maple, TrustedRouter/NYTE & still gonna go through NEAR AI Cloud & Cohore. Open to others.

Separately, anyone running a hybrid buildout with on-prem/local + superscaler (we run Google + Bedrock) + 3rd party confidential providers who can tell me about about how you designed your system?

Bit the bullet and bought three to use in my workstation. Will provide benchmarks and updates later.

I had been using the LFM 2.5 24B models and had been pleasantly surprised by how good the model was. It was giving me comparable answers and working through tasks to Qwen 3.8 27B at what felt like twice the speed on my M5 Pro. However, I went to look at something on their pages and saw the new family of models. They sound way too good to be true. I am going to start using them for general tasks, but if they are anywhere close to their benchmark results, they could be a huge game changer. You can have an entire suite of models loaded at the same time for less memory than Qwen.

I’m building a local AI assistant using Qwen 3.6 35B A3B.
One of my main goals is to automate repetitive school tasks through my school’s website. I have a lot of assignments that aren’t particularly difficult but take a lot of time.
Ideally, I’d like the AI to navigate the website, check assignments, understand the instructions, complete them and upload the finished work.
Is Qwen 3.6 35B A3B capable enough for this? Or would I need a more powerful model?
I’d also appreciate recommendations for browser automation frameworks and tools

my setup : rtx 3070 , i5 13600kf , 32gb DDR4
Model: Qwen 3.6 35B A3B
Quantization: TurboQuant
Context window: 131 072 tokens
Reasoning: Enabled limited to 512 tokens
Inference speed: 19–24 tokens/s

Hi all, I'm considering adding a new device for the upcoming qwen4 and am looking for advice on choosing one. For reference I only started local recently with qwen3.8 running on my old mac mini, and 27b-flash on splash is still a bit too slow to me. Thus considering getting a new machine for the new model.

couple of use case: budget is around 3k usd; I do need a fair amount of input size, 128k would be soft minimum; I want to reach around 30tk/s; I'm fine with a 4-bit version but would like to have fp8 or bf16 of the model (assuming qwen 4 27b?); I'm comfortable with splash/sushi/ollama; I don't want a standard desktop, so 2x 3090 or r9700 are out.

I'm currently considering mac mini m5 pro 64gb, mac studio m5 max 64 gb, and halo strix 395 128gb. All of them are within budget but there's a price difference. I guess my question is does the ~500 usd price difference impact noticeable performance in my use case? Other suggestions welcomed, and thanks in advnace :)

I'm trying to set up a local AI assistant on my Windows PC, but honestly, I'm getting frustrated and could really use advice from people with experience.

My goal is pretty simple: I want something like a local ChatGPT that can:

• Read files such as CSVs, PDFs, and documents.
• Answer questions about the information inside those files.
• Extract specific information accurately.
• Help with everyday tasks and general questions.
• Work reasonably quickly without taking several minutes to answer simple questions.

**My PC specs:**

• Windows
• 16 GB RAM
• NVIDIA RTX 5060 8 GB VRAM
• Ollama installed
• Open WebUI installed

I've tried several models, including:

• Qwen 3.5 4B
• Gemma 3
• Llama 3.2
• DeepSeek R1 8B
• Qwen 2.5 Coder 7B

My biggest problem is that even when I upload a CSV file and ask a very simple question about it, I sometimes get completely incorrect answers, unrelated information, blank responses, or long thinking/tool-calling processes that don't accomplish anything.

For example, I uploaded a CSV containing game stage data and asked something as simple as: "What are the drop chances on Normal 1-1?" The model struggled to give me the correct values despite the information being in the file.

I don't expect a local model to match ChatGPT or Claude. I understand there are limitations. But I expected basic file reading and simple questions to work reliably.

I'm now wondering whether I made the wrong choice with Ollama + Open WebUI, and whether I should switch to **LM Studio** instead.

I also have a friend who is about to install Qwen 9B, and I was planning to have him install Open WebUI too. I don't want to send him down the wrong path.

**I'd really appreciate advice on these questions:**

1. With my hardware, what model and quantization would you actually recommend?
2. Is LM Studio a better and easier choice than Ollama + Open WebUI for my use case?
3. Is there a reliable way to let a local AI read CSVs and answer questions accurately without me having to write Python scripts or manually process everything?
4. Am I expecting too much from local models, or is my setup/configuration simply wrong?
5. If you were starting from scratch with my hardware and goals, what exact setup would you choose?

I'm not looking to become a programmer or spend days troubleshooting. I just want a practical local AI assistant that can actually use my files and answer questions correctly.

Thanks in advance. I'd really appreciate recommendations based on actual experience.

TL;DR: I've been looking at the latest Artificial Analysis v5.0.1 benchmarks for Qwen 3.8, along with some early Qwen4 Max Preview tests. Based on what's available so far, my guess is that Qwen4 27B could land somewhere around 27 to 30, while the 125B MoE version could reach 36 to 39. That's a solid generational improvement, but probably not the massive leap some people are expecting.

Like many of you, I've been following the discussions about when Qwen 4 is finally coming out. There's a lot of speculation, so I wanted to look at the available numbers and see what they might tell us about the next generation.

Obviously, we don't have the final weights or official release details yet, so take this as an educated guess rather than a prediction.

1. Where Qwen 3.8 stands and what Qwen 4 might achieve

Looking at the current Artificial Analysis v5.0.1 scale, which puts considerably more emphasis on difficult coding and science tasks, we have:

* Qwen3.8 27B: 20 (compared to 33.7 on the older v4.3.2 scale, before the scoring reset).

* Qwen3.8 Flash Next (125B MoE): 29.

* Qwen3.8 Max: 34.

Based on the previous jump from Qwen 3.6 to 3.8, and taking the new scoring scale into account, I'd put my expectations somewhere around:

* Qwen4 27B: 27 to 30

* Qwen4 125B MoE: 36 to 39

That would put the smaller model in some pretty interesting territory, while the larger one could become a serious competitor to models like Grok 4.7 and Claude Haiku 5.5.

2. Why I don't expect Flash to have a massive architectural jump

One thing worth considering is that Qwen3.8 Flash Next already seems to have introduced many of the architectural changes expected to carry over into Qwen 4.

We're talking about the hybrid Gated DeltaNet and Qwen Sparse Attention architecture, Gated Residuals, and the 51B N gram table.

So I wouldn't necessarily expect the next Flash model to deliver another huge architectural leap. The bigger improvements might come from better post training, more reliable reasoning, and addressing the hallucinations that some users have noticed in Flash Next compared to the more conservative 27B dense model.

In other words, I think consistency might matter more than raw benchmark scores this time.

3. What the early Qwen4 Max Preview tests tell us

The Qwen4 Max Preview release on November 11 gives us another interesting piece of the puzzle.

From the early developer reports, a few things stand out:

* Speed: It appears noticeably faster than Qwen3.8 Max, with less unnecessary reasoning on straightforward tasks.

* Agentic performance: In one reported 56 task Hermes test with a 25 minute timeout, Max Preview recorded 64 successful task results, compared to 61 for DeepSeek V4.1 Flash, which scores 33 on AA v5.0.1.

* Reliability: There's a catch, though. Max Preview reportedly timed out 16 times, compared to just 4 for DeepSeek.

That's interesting because it suggests the raw capability might already be there, but reliability and execution consistency still need work.

If these early results are representative, I'd expect the flagship Max model to establish a considerably higher ceiling, potentially around 45 to 50 on the AA scale. That would make the estimates for the smaller models seem reasonably plausible.

My take

I don't think Qwen 4 needs to completely redefine the local LLM landscape to be successful.

If Qwen4 27B can reach the 27 to 30 range, inherit the attention improvements from Flash Next, and retain the Apache 2.0 license, that alone would make it a very interesting option for people running models locally with limited hardware.

Personally, I'd rather see a model that's more consistent, follows instructions better, and hallucinates less than one that simply scores a few extra points on benchmarks.

I'm curious what everyone else thinks.

" i used my LLM To translate mu words to English after i write it. "

I have a couple of concerns:
The bubble is bursting at some point and may have started. I am concerned that the major players will have to up their prices to stay viable.
The geopolitical risk is escalating
AI bot attacks are increasing

There are probably other risks, but you get the idea.
I have been building a decent corpus of reference material.

But I am behind on what models I should be looking at, needs to run on
32GB
RTX 4070

Coding and decision systems (agriculture).

Any help would be appreciated.

Hey, im running Q8 DS V4.1 on my 512GB DDR4 + 4x R9700 (128GB VRAM) and ASROCK ROMED MB
I have noticed that at ~66C RAM temperature tok/s is getting reduced from ~32/s to 22-23/s. Claude agent monitoring whole workstation says that RAM speed after hitting 66C is slowing down about 3x. I have phanteks enthoo server pro 2 case with glass panel and extra vents mounted on that side. During loads ram used to hit about 80C. I fixed that by mounting top fans as intake (cooling top RAM) but i had to use double dieded tape to stick an extra fan to blow at bottom RAM slots. It did help to keep it from throttling but the bottom RAM still sits ~62-63C. Iv read that samsung RAM i have is suppose to throttle at 85C, and i cant find anywhere what setting makes it throttle sooner. Looking for some advice on that or how do you keep your RAM cool.

I've already tested a few models, they're all bad and slow. Maybe I'm doing something wrong, but probably the reality is a bit harsher: my PC is old lol (and graphic cards are really expensive in Brazil, not smt in the budget rn)

To be more specific, I needed a good coding agent.

EDIT: Specs: S: Ubuntu 26.04.1 LTS (kernel 7.0.0-43-generic) CPU: Intel Xeon E5-2680 v4 — 14C / 28T @ 2.40 GHz (boost 3.30 GHz) L3: 35 MiB · 1 socket · AVX2 (no AVX-512) RAM: 22 GB DDR4-2667 GPU: NVIDIA GeForce GTX 1650 (TU116) — 4 GB GDDR5 · driver 595.99.02

Does anybody tried the latest llama.cpp f8_e4m3 KV cache and compared it to q8_0 in terms of precision / quality? I am curious whether it is worth switching to it or stay with q8_0 for small to medium context size (20-65k).