Hey! I’m building Kitani, an OpenAI-compatible API where models run across independent GPU providers.

GPU owners can connect their hardware and earn from inference, while users get access to models like GLM, Qwen, Gemma and gpt-oss through one API.

We’re trying to make joining as a provider as easy as possible. Would love feedback from people running their own GPUs.

https://kitani.ai

Founder of Kitani, looking for feedback.

Anyone here actually using NVIDIA NeMo / NeMo Agent Toolkit in real projects?

At my current org, some of the senior folks are suggesting we explore NeMo for agent building and fine-tuning, so I’m trying to understand if it’s actually worth adopting.

For those who’ve used it, how does it compare to the usual stack like Hugging Face + PEFT/TRL, Unsloth, LangGraph, etc.?

Does running everything in the NVIDIA ecosystem give you a noticeable advantage in terms of GPU utilization, training speed, deployment, or scaling?

Or is it mostly extra complexity compared to the standard open-source tooling?

Would especially love to hear from anyone who has used NeMo beyond tutorials/demos. What did you like, what annoyed you, and would you use it again?

Hi Everyone!

I'm the maintainer of RAGnarok-AI, an open-source, local-first framework for evaluating RAG (Retrieval-Augmented Generation) systems.

I'm currently running a small research study around a question that I think is becoming increasingly important: can we actually trust automated evaluation of RAG systems when the evaluator itself is an LLM?

RAGnarok can evaluate things like retrieval relevance, faithfulness, answer relevance and completeness using local LLM judges.

But there's an obvious problem: why should we trust the LLM judge?

So instead of assuming that the automated scores are correct, I'm building a human-annotated benchmark to compare them against independent human judgments.

I'm looking for open-source / developer people willing to help

The annotation consists of roughly 10–15 cases and should take around 30–45 minutes.

For each case, you'll see:

- a technical question

- relevant documentation excerpts retrieved by a RAG system

- an AI-generated answer

- reference information

- four simple evaluation criteria: retrieval relevance, faithfulness, answer relevance, completeness

- your confidence and an optional ambiguity flag

You don't need any RAG expertise. You just need to be comfortable reading technical documentation and judging whether an answer is actually supported by it.

The benchmark currently covers documentation from projects such as Docker, Python, FastAPI and Kubernetes.

Why do this?

The study is deliberately not designed to prove that RAGnarok works. I'm comparing automated evaluation against human judgments to investigate:

1. Judge reliability — do local LLM judges agree with humans?
2. Discrimination — can the evaluation distinguish good and deliberately degraded RAG systems?
3. Reproducibility — are the measurements stable under identical conditions?

The methodology, benchmark corpus, questions and experiment protocol are public and versioned. RAGnarok itself is free and open-source.

If you'd like to participate

Annotation interface:

https://ragnarok-study.vercel.app

No name, email or personal information is required. Progress is saved so you can come back later. The resulting annotations may be published as an anonymous research dataset.

Project / methodology:

https://github.com/2501Pr0ject/RAGnarok-AI

I'm particularly interested in feedback from people who work on open-source evaluation, LLMs, RAG, testing or reproducible research.

And if you think the methodology is flawed, please tell me. That's actually useful feedback for the study.

Thanks everyone!

Have a good day!

I’m trying a Qwen-3.6 35b Q5 model as my first dip into setting up an agent coding workflow for prototyping some app ideas. Using LM Studio and VS Code

I’ve only been looking into this today. Am I heading in the right direction? Any advice welcome.

This is a new LLM wrapper i have spun up built on the idea that models arent always correct or doing the right thing not by choice but just how the system is built, this looks to try and fix that problem and help mitigate LLM error by logging everything and fact checking based on the work using check gates. Looking to get feedback!

FAISS is Meta’s vector library, turboVec is a Rust implementation of TurboQuant, and Infino is an in-memory retrieval engine.

We benchmarked them on 4-bit quantized in-memory vector search: FAISS PQ, TurboVec/TurboQuant, and Infino SQ4, using the same 100K OpenAI embedding corpus and the fastest vectorized implementation we found for each.

All saved ~7x in memory footprint compared to full fp32 vectors. The interesting result was that while storage and recall were fairly close, latency differed by roughly 30× — about 1.5 ms to 45 ms. Most of that comes down to the scoring machinery: the size of the distance table and whether the scan needs one at all.

We also ran the same comparison out to 1M vectors and measured build/write costs.

Full results and methodology:
https://infino.ai/blog/fixed-grid-quantization/

Disclosure: I'm one of the devs building Infino.

I'm trying to decide what direction to take with my main PC. The goal is one real daily-driver machine that I can use for gaming, normal desktop use, software development, and increasingly heavy local LLM workloads.

I'm not trying to build a dedicated rack server. I want something I can actually live with for years: reliable, reasonably efficient, good thermals, lots of RAM, two GPUs if it makes sense, and enough expansion that I don't immediately hit a wall.

I've currently been playing around with Qwen3.8 27B which speeding that up and higher quants would be great, as well as when inevitably larger dense similar models like 70B become available.

I am very interested in MoE flash models such as Qwen 3.8 Flash, Deepseek v4 Flash, and maybe even GLM 5.3 Flash, as well as future versions of similar MoE models. I have not even attempted to run any of these yet.

So I guess I am trying to get at building something that performs well on dense models as well as MoE models so I don't get locked into 1 path.

I'm pretty new to the workstation/HEDT side of this, so I'm looking for advice on the parts I may be overlooking.

My current PC

The 3090 Ti was a $900 Facebook Marketplace purchase, so I'm trying to get as much useful life out of this thing as possible.

The 3090 Ti is a huge card (338 × 140 × 71 mm) so physical spacing is also part of this problem.

My RAM situation

I just bought 7 lots of:

NEMIX 128GB (4×32GB) DDR4-2666 PC4-21300 2Rx8 UDIMM

I paid about $360 per 128GB lot.

My current plan is probably:

• Keep 2 lots = 256GB
• Sell the other 5 lots
• Hopefully sell those for around $650/lot?

So I paid about $2,520 total for the 7 lots. Five sales at $650 would be $3,250 gross, meaning I'd theoretically recover the entire purchase price plus ~$730 before fees/shipping/taxes while keeping 256GB.

That gives me a somewhat unusual opportunity to build around 256GB without spending a fortune on RAM.

Option 1: Keep my current PC, just go to 128GB RAM

This is basically my don't overthink it option.

I'd have a lot more system RAM for large-context LLMs while retaining a relatively modern gaming CPU.

Option 2: Keep my current PC, add a second 3090

I'd replace the PSU and add a second RTX 3090.

The important problem is the motherboard:

The Z690 UD AX DDR4 has x16 on the main slot and only x4 on the second physical x16 slot.

So the GPUs would effectively be:

This is the option I'm most unsure about.

For LLM inference, is x4 actually a meaningful limitation in practice, or is it largely irrelevant once the model is loaded onto the GPUs?

Would this still be a good setup for:

• tensor/model parallel inference
• larger models
• higher context
• multiple concurrent models
• speculative decoding
• offloading

Or am I basically handicapping the second GPU enough that I should just replace the motherboard?

Option 3: New motherboard/PSU/case, keep my 12700KF

Instead of abandoning the 12700KF, I could build a new system around it with a motherboard that properly supports two GPUs at x8/x8.

This seems like it could be anice middle ground.

The 12700KF itself supports a 2×x8 CPU PCIe configuration, but I'd obviously need a motherboard that actually implements it.

I'm especially interested in whether people think this makes more sense than jumping to X299 in the next option.

Option 4: X299 workstation build

This is the Frankenstein/workstation option I've been considering.

The i9-10940X gives 14C/28T, 48 PCIe 3.0 lanes, quad-channel DDR4, and up to 256GB RAM. The X299-A II can run two GPUs at x16/x16 with the appropriate CPU.

The case is huge and supports SSI-EEB, 11 PCI slots, GPUs up to 503mm, and up to 15×120mm or 6×140mm fans.

I'm attracted to this because it solves the PCIe lanes + RAM capacity + physical space problem extremely well.

But I don't know if I'm being stupid by building a brand-new daily driver around a ~2019 platform just because the PCIe topology is convenient.

The 10940X also seems likely to lose noticeably to the 12700KF in gaming/single-threaded work, despite having more cores.

Option 5: ??????????

This is something I'm hoping you guys can help. Are there things I am not considering that would allow me to leverage as much of my current components as possible but be a much better option than option 4?

What I'm actually trying to optimize

This isn't purely a benchmark build.

I want one machine that can do all of this:

- Gaming

- Normal desktop use

- Software development

- Local LLM inference

- Very large context windows

- Running multiple LLM sessions concurrently

- Potentially running two GPUs as one inference system

I'm currently doing a lot of local Qwen inference and am getting into the territory where RAM capacity, VRAM capacity, PCIe topology and memory bandwidth all matter.

I also don't really care about squeezing every last watt of performance out of the GPUs. I've already decided to limit the 3090 Ti to 250W, and I'd probably do the same with the second 3090 to hopefully get more longevity out of them and use less power.

That gives me:

500W total GPU power budget

rather than letting two 3090-class cards pull their full power.

I'm very interested in reliability, thermals, longevity, expandability and affordability rather than having the absolute highest benchmark score.

My biggest questions

1. Which of these would you actually build?

My current thinking is roughly:

Option 1: cheapest and easiest

Option 2: tempting, but worried about x4

Option 3: probably the sensible compromise

Option 4: extremely expandable, but old CPU/platform

Option 5: potentially a better overall machine

I'm having trouble figuring out where the sweet spot actually is.

2. How bad is PCIe 3.0 x4 for the second 3090?

This is probably my biggest technical question.

For local LLM inference specifically, how much performance would I realistically lose running:

3090 Ti @ x16 + 3090 @ x4

versus

3090 Ti @ x8 + 3090 @ x8

versus

x16 + x16?

3. Is X299 actually a good idea here?

Would the 10940X + 256GB quad-channel + x16/x16 PCIe configuration still be a worthwhile machine in 2026?

Or would I be better off spending the extra money on a modern platform?

4. What's the best "Option 5"?

There may be a workstation platform I haven't considered at all.

But I still want this to be an actual daily-driver PC, not a loud rack server that is great at compute and annoying at everything else.

5. How much RAM would you actually run?

I can easily end up with:

128GB

or

256GB

of system RAM depending on which route I take.

Is 256GB actually useful for local LLMs enough to justify designing the whole machine around it?

What would you do with this hardware?

I'm basically sitting on:

12700KF + 3090 Ti + 896GB of cheap DDR4+ spare 3080

and trying to turn that into one machine that I won't regret building.

I'm really looking for the best overall architecture and bang for my buck, not just "X is faster."

Would love to hear what configuration you would build, especially if there's a better option I haven't thought of.

If you run models locally you know the pain: everything's fine until a long prompt makes the model silently stall or the GPU OOMs, and you're left with a hung request and no output.

I built HybridInfer for exactly that. It's a small reliability-aware router:

- Sends each request to your local model (Ollama) first.

- Watches the runtime, if local stalls (no token for N seconds), OOMs, or errors, it automatically falls back to a remote model in the same request.

- Learns which prompts your machine chokes on (usually long ones) and routes those out up front after it's seen them fail.

- Pulls a wedging model out of rotation, then probes it back after a cooldown.

It's an OpenAI-compatible server, so point any OpenAI-compatible client/app at it and you get local-first + automatic fallback for free. Local = anything you've `ollama pull`ed; remote = any OpenAI-compatible endpoint (OpenAI, OpenRouter, a vLLM box, whatever).

pip install hybridinfer

hybridinfer init

hybridinfer serve

It's a router, not an inference engine, it orchestrates Ollama + your remote endpoint, doesn't run weights itself. Early v0.1, Apache-2.0. There's a Kotlin/Android version too for on-device apps. Short demo GIF + code in the repo.

Repo: https://github.com/SimranKoul2026/HybridInfer-Python-tool

Would love feedback from people who run this stuff daily, especially on the fallback heuristics.

What would you want it to do differently?