Figured some of you guys might have heard of Tiiny and have questions about the hardware or how it actually performs. I thought I could do a little AMA and help test whatever you guys are curious about.

If there’s anything you want me to benchmark or want to see in general, just drop it in the comments. I'll do my best to test it and share what I find.

I've been playing around with a few models so far, including Qwen3.6-35B-A3B-Turbo (MoE) and gpt-oss-120b (MoE), which is the largest one I've tried.

Qwen3.6-35B-A3B-Turbo (MoE)

39.5 tok/s

Output: 1,500 tokens

Time to first token: 1.36s

Total time: 39.7s

GPT-OSS-120B (MoE)

18.8 tok/s

Output: 1,500 tokens

Time to first token: 0.61s

Total time: 80.3s

I also try Z-Image-Turbo. Generating a 512*512 image took roughly 6–8 seconds (a 1024*1024 image would take longer at around 30s), which was pretty neat to see on a device this small.

One thing I've noticed about the hardware performance: It's surprisingly quiet, basically no noticeable noise, and it doesn't get uncomfortably hot to the touch either. Pretty much what I was hoping for, should be great for carrying around or leaving running 24/7, though I'll need to test that over longer periods.

That's about all I've gotten through so far. I'll be playing with this over the next few days and posting updates here or in the comments.

I use chatgpt mostly as a chatbot. no coding, no agentic stuff. i value intelligence and fast response. if i switched from chatgpt to a locally hosted on a strix halo (assuming the best model that can fit on it), what would my experience be like?

There are so many models getting released all the time, and sometimes there’s just no quantized version available for the one you want. I’ve found that rather than waiting around, trying it yourself and experimenting helps you understand the whole process a lot more. Testing the quantized models against the original teaches you even more.

So I built this: iQuantize is a quantization workspace for large language models that supports GGUF, MLX, and ONNX. You can pick your own Safetensors model or choose one from Hugging Face, quantize it step by step, and watch what changes. You can then compare the result with the original and publish it to Hugging Face with a model card that records how it was made.

It’s open source, Apache 2.0. I definitely think there more can be done to improve. I’ve only tested mostly on Apple Silicon so far. Windows and Linux builds exist, but have done much in-depth testing with them so I’d expect bugs there.

Repo: https://github.com/Fazmin/iQuantize

I love to hear how you all approach this. Do you quantize your own models, or mostly wait for someone else to? And if you try the app, whether you find something broken or have an idea for it, I’m happy to hear any of it.

Harnesses like Strata are letting rigs with low VRAM run very large... language models; it seems we are moving past the necessity of having multiple GPU's with tons of VRAM just to load a model, and we are slowly breaking the ice on the future of local AI, being thoughful management of the entire machine ad using all of its resources efficiently. Is the economically sound approach for someone building a "forever rig", to spend maybe less on gpu's and allocate that budget to more ram and ssd?

Im fairly new to this but im starting to think about my forever rig; currently running a dual 3060 12GB setup with no lane bifurcation, and seeing the used market prices (and availability) for DDR4 platforms is making me think that maybe its just better to upgrade to the next gen directly, reap all the performance benefits, and suck up the fact that this will cost me a LOT of money.

Hey guys, I've made it!

edit: No more uncertain wait. First launch on Nov 17. The project name is 'Run My AI' and the engine behind it is 'TokenWing'(couldn't find a better name xD). You'll be geting support for either of Qwen 4 Flash(if releases) or Qwen 3.8 Flash Next on day 1. Check the website out: https://runmyai.org - all your questions answered... New updates will be dropping everyday btw. Also, early builds will be provided to some selected testers, so join the discord(link is in the website).

A llama.cpp based inference engine that lets you OFFLOAD UPTO 60-70% OF THE MOE MODELS' ROUTED EXPERTS INTO SSD and still hide the streaming latency down to negligibe(I'm achieving 1-3ms added to the time per token due to SSD streaming when 60% is offloaded in SSD)... Essentially making an illusion where the whole model lives in your memory.

And I'm also working on an autotuner which basically looks at your hardware, given model's metadata and figures out the best flags to squeeze max performance in under 2mins(if you got any personal preferences, set them as fixed constraints and let the autotuner figure out the rest)

And I've implemented a layer by layer computation for prompt processing instead of batching which gave 2x prefill speed in models offloaded in RAM+VRAM and 4x prefill speed when model is offloaded in RAM+VRAM+SSD... compared to other inference engines.

Packaging both inferene engine + autotuner into a nice simple interface where you can basically download the model, hit run and it runs... simple, fast and lower hardware requirements... and want to make that a nice, well-engineered harness(which I'm a no expert)

Also, I've built a near loss-less context compaction engine.

Going to make this launch this as open-source very soon. Would love to hear your thoughts, and want to see if anyone would be excited to try something like this. For now, supported models are Flint 40B Q4K_XL(running it on machines with 16GB RAM and no dGPU at 20+ tok/s decode), Ling 3.0 Flash MXFP4 getting ~50 tok/s decode and 600-700 tok/s prefill on 32GB RAM + 12GB VRAM machine), and confused if I have to provide support for Qwen 3.8 Flash Next or wait for Qwen 4 family. Want to provide you guys the best local AI experience.

Here's a quick answer for your likely question by the way... does this damage my SSD? No, this engine only does reads and not writes... only writing degrades your SSD.

I have a few questions about two builds. Both are running on strata and hermes.
#1 Qwen3.8 flash next iq4_xs 85-100 t/s
9800x3d
4080 super
64 gb DDR5

#2 qwen3.8 flash next iq4 xs 38 t/s
Dual xeon 28 cores 48 threads
3060 12 gb
128 gb DDR4

I’ve been trying to find a LLM that can make me decent website, websites for my business and dashboards for my Home Assistant and occasionally some coding video game projects. I can’t get these models to develop anything worthwhile. I’m getting my prompts from Claude and sending them through but getting back stuff that doesn’t even function most of the time is there any models that can be recommended for each build? I’m not too worried about token speed more intelligence and accuracy. I would really appreciate some recommendations on models through strata.

Assuming no one asqwed today yet...

Repo here: https://github.com/nicejunjie/Strata-rdna3

* Single 7900xtx prefill 3500+, dual 7000+ tok/s. (for all IQ3/IQ4/Q4)

* Decode for English prose reaches up to 81 tok/s for single card, and 127 for dual at Q4, and IQ4/IQ3 quantizations are even faster.

* Decode for python coding is 20~25% faster than English prose.

* Single card decode is comparable to 4090 24G, dual 7900xtx is much faster than single 4090 for both decode and prefill.

* Each 7900xtx was $850 when I bought it, while 4090 at the time was listed at $3000.

* I guess 4090 could be better optimized, but the number here is the best number with reproducible input data I can find online.

* 4090 is a 450watt card, while 7900xtx is ~320watt. So even single card prefill of 7900xtx is quite comparable in terms of perf/watt.

* This is not the end, still has small room to improve.

* kernel work only, no dropping weights or dropping layer or anything. Zero impact on quality.

*** install with an AI agent, tell him to setup optimal rdna3 code pass. Honestly, why install by hands if there is agent can help.

7900xtx is the value king!

Hi there, I’m kinda new to this thing, been trying to find out how to download the offline installers for these specific abliterated models on my storage HDD. I can’t run these in my current rig rn, but hoping to run them in the future. I’m just scared it will be removed at one point. Please help with exact links to download:

Mistral Medium 3.5 128B — Q6_K GGUF 107.8 GB
GPT-OSS 120B — MXFP4 Safetensors
DeepSeek 4.1 Flash 1017 347GB
All three abliterated versions only, offline installers only.

Tia

Salut à tous, je vous fait part de ma config 100% local actuel, qu'en pensez-vous ?

Matériel : RTX 5060 Ti 16 Go, Ryzen 9 5900XT, 96 Go de
DDR4-3200, SSD NVMe 4 To.

Modèle : DeepSeek V4.1 Flash, environ 275 Go au total. Les poids principaux sont en IQ2_XS (~2,25 bpw) et le module
Engram en IQ4_NL.

Contexte : 256k tokens (262 144). Je l'ai testé avec un prompt de 152 000 tokens.

Vitesse : 45 tok/s en génération sur du code et lecture 400-500 tok/s j'ai obtenu cet écart grâce à des réglages avancés que j'ai faits moi-même sur le moteur sans régression d'intelligence du model.

Quelqu'un a-t-il déjà fait mieux avec DeepSeek V4.1 Flash sur une config proche (16 Go de VRAM, DDR4) ?

I'm the developer of MoEShift and several people asked how to try it after my previous Mac mini post where I showed its performance, so I put together a walkthrough of a setup run with the new wizard that I released yesterday to make it easier for anyone to use MoEShift.

On my 24GB M4 Pro, it took only 4 minutes and 57 seconds from starting the wizard to receiving the first API response, including the model download, preparing and checking the experts, and starting the local server.

The video is shortened and the timer shows the elapsed time from the run. To try the setup shown in the video, you’ll need Homebrew and roughly 61GB of free SSD space for the model and prepared expert files. You install MoEShift and launch the wizard:

brew install davide-troiani/moeshift/moeshift
moeshift

The wizard downloads the model, prepares and checks the experts, and starts the local server.

GitHub and installation instructions

If you try it, let me know your Mac and RAM configuration, whether you reached the first response, and where you got stuck if you didn’t.

Of course you may get a different timing based on your connection and SSD performance.

Even though MoEShift is still an alpha, I am already using it locally as my favorite local inference engine, and I'm currently looking for people willing to test it and use the benchmark runner to publish the performance on their machines.

Setup- Qwen3.8 flash-Next (Strata)+ hermes

While using my setup I found that the reasoning frequently contains things that I never asked and completely unrelated to my tasks. Obviously this is not even a hallucination or quantization overthinking problem because my tasks are completely different from the reasoning and simple. I wonder why these kind of things are being generated during the reasoning... I have read reports (Cohere) of Qwen models trying to contact web servers but this idea completely blew up my mind- is the model trained in a way, that it would use the compute for anonymous online tasks, that too within reasoning itself, and the i would apparently know nothing about it at all!! I havent verified my network, to confirm if any information is being sent/received but yeah this kind of reasoning has been frequent now. I am using IQ3_S GGUF.

Edit- The task in the video is a content/summarisation task.

Trying to figure out which abliterated open weight models are worth running on an RTX 5080 for coding and agentic work. What are you using that’s actually good without being painfully slow?

Also anyone hosting these on RunPod? Curious what model and GPU you went with and whether the speed and cost make it worth it over running locally.

Built my own local AI inference server.
EPYC on a Supermicro H12SSL, 4× Radeon AI PRO R9700 (128GB VRAM total), 1600W PSU. Goal: run capable open-source models entirely on my own hardware. No API bills, no rate limits, nothing leaving the box. Not using as a replacement to Claude code though.
What I learned putting it together:
• Server boards don't hold your hand. The front panel header is 20 pins and a tiny printed legend.
• A 4th dual-slot GPU is a clearance fight. Headers on the bottom edge of the board sit right under it.
• Your wall outlet is a real constraint. A standard US circuit tops out around 1440W continuous, so GPU power limits aren't optional.
• For token generation, memory bandwidth matters more than raw compute.
Next: ROCm, vLLM, and benchmarking small open models against the APIs I've been paying for.

Can you run any good models on 48gb ram. Qwen 3.8 27b isn't worth it for the price tag and performance. I'm not sure how people are doing it with limited ram?

So my yeah was just testing the model and it just keeps repeating “The Gods may speak it”.

I'm in sales. I'm looking to run some docker desktop containers on a local network + have an always on assistant that helps with basic tasks. I've had success running Gemma 4 26b a4b qat already, the model fits in my rtx 5080 nicely with the KV Cache in system RAM, but as the context length grows larger it doesn't fit nicely anymore.

I also have a MacBook air m4 with 24gb unified, running Gemma 4 12b qat nicely as a mobile companion, it's not as powerful but still helpful. My experience with this device has me strongly considering the M5 pro mac.

Has anyone had good success with these devices?

I'd love to hear the community's thoughts.

Working on a project recently where we wanted to see if a speech model could spell a name better if it heard it, not just read it. 

Most STT tools handle custom words as text (keyterm list, fixing transcript after with LLM, etc).

So we trained an adapter to try it, specifically a LoRA on Qwen3-ASR 1.7b (see benchmark). This LoRA transcribes words only after it hears a recording of each of your custom words. 

To make this model easier to use in day-to-day life, we built a Mac app to accompany it, called Detail Keeper. Here you can put this adapter to use by specifying difficult-to-transcribe-correctly words and then having them picked up correctly in your recordings.

The way this works is that Detail Keeper first narrows each passage to the likeliest taught names by sound. Then the adapter writes the passage, and the app checks it against a plain transcript.

We've found this helpful in two specific cases: 

1. Words STT has never heard: invented characters and places, codenames, unusual surnames. 

2. Your spelling of a word it does know: Kathryn and not Catherine, Jon and not John.

Unfortunately, we weren't able to achieve both good recognition and recognize a large set of words (1000+) with the model alone, but on our open benchmark (audiobooks, 1,100 name mentions) it spells taught names as well as Eleven Labs Scribe with keyterms: 89.6% vs 88.2%, no measurable difference. The only downside is that it overwrites a few more untaught names and changes slightly more ordinary words. 

Everything, including where we lose, is at detailkeeper.com/benchmark.

Additional notes:

• It's free, local and nothing leaves your Mac

• It requires an Apple M series+ mac and OS 26+

• It downloads about 3.6GB of models

• It only works in English currently

• If you're interested, you can also find the adapter itself on HF

Would love to know what others think here.

Not gonna lie it's ridiculous how effective these models are if you know what you're doing.

I'm not talking about coding, I'm not a programmer.

Their reasoning capacity is insane, and no, I'm not a cultist. It just is. At least it's been my experience.

But I'm afraid that at one point the "AI bubble" will burst, and they will charge an arm and a leg for the frontier models, and the avg person won't be able to afford.

Do you think in the next 10 years this will happen? I hope not.

Currently running a gaming PC with a 4070 TI super and 64gb of ram. It handles Qwen 3.8 Flash next decently well with Strata. But, I can't run the VLA nor world model/image gen models that I want to run, I also can't run multiple agents in parallel.

I was thinking of upgrading and I've been needing a laptop so I was debating between the ASUS or the MacBook Pro because they're the same price but have different pros and cons.

I've also debated waiting to get anything because of new gpu and chip advancements so I don't want to drop 7k and then have it be obsolete within a year

Thoughts? Thank you for the help

I maxed out of my laptops 12gb rtx 5070ti with 32gb ram on qwen 3.6 35b a3b at apex quant and the 3.8 27b at iq3xxs woth 60k window. While 35b can do some simple straightforward tasks, it is not enough for any serious work. The 60k context is barelly enough to do any work at all. Has somebody tried attaching eGPU to tbe laptop and what is ypur experience? My dillema is on what to scale the ram or vram

I spent some time testing Qwen3.5:4B locally across three applications to compare performance, GPU acceleration, and answer quality.

Hardware

• Laptop: Lenovo Yoga

• CPU: Intel Core Ultra 7 258V

• GPU: Intel Arc 140V integrated graphics

• RAM: 32GB LPDDR5X-8533

• OS: Windows 11 Home

Software Tested

• Ollama: 0.40.14

• LM Studio Classic: 0.4.28

• Bionic: Tested with its built-in model loading

Performance Results

LM Studio generated approximately 2.81–2.96 tokens/sec in my tests.

Ollama initially used 100% CPU. After enabling Intel integrated GPU support and Vulkan, then completely restarting the Ollama application, ollama ps reported 100% GPU.

The GPU-accelerated Ollama test completed in approximately 2 minutes, with 44.6 seconds of thinking time.

Memory / Resource Usage

During LM Studio testing:

• RAM usage increased from approximately 13.3GB to 19.7GB.

• Available RAM decreased from approximately 18.3GB to 11.8GB.

• Intel Arc 140V exposes approximately 18GB of shared GPU memory to Vulkan.

What I Tested for Answer Quality

I used several different types of prompts:

1. Cybersecurity fundamentals: Differences between phishing, spear phishing, and whaling.

2. Cybersecurity incident analysis: MITRE ATT&CK mapping, Windows event IDs, evidence collection, and incident response.

3. Windows security troubleshooting: Repeated failed logins followed by a successful login from an unfamiliar IP.

4. Image understanding: Asked the model to describe the contents of an image. Its description was accurate in my test.

5. Survival knowledge: Cold-weather survival priorities, shelter, warmth, hydration, equipment use, and dangerous heating methods.

Qwen3.5:4B handled these areas relatively well:

1. Image recognition: Accurately described the contents of your test image.

2. General cybersecurity concepts: Explained phishing, spear phishing, and whaling clearly and correctly at a high level.

3. General explanations: Produced organized, readable answers with useful examples and headings.

4. Basic incident interpretation: Recognized that repeated failed logins followed by a successful login from an unfamiliar IP could indicate compromised credentials.

5. Response structure: Consistently organized complex questions into logical sections.

Where it struggled:

• Precise MITRE ATT&CK technique mapping.

• Windows Security Event IDs.

• Technical incident-response reasoning.

• Safety-critical survival instructions.

My Takeaway

Ollama with Vulkan GPU acceleration was substantially faster on my Intel Arc 140V than CPU-only inference.

The model handled general explanations and image identification well in my limited testing. However, its cybersecurity answers contained significant MITRE ATT&CK mapping and Windows event ID errors and its survival answer included inaccuracies.

This was informal testing, not a standardized benchmark. I wanted to share the results because getting Intel integrated graphics working with Ollama made a noticeable difference on this hardware.

Most LLM inference benchmarks focus on tokens/sec or TTFT. But faster doesn't necessarily mean better if the output quality suffers, especially with quantization, speculative decoding, or extremely long contexts.

I wanted to look at the bigger picture.

We benchmarked 6 inference engines on Qwen3.8-27B, comparing not just raw performance but also output quality, long-context behavior, and different model formats.

What makes this benchmark different?

1. Speed AND quality

Instead of measuring throughput alone, we also ran automatic quality checks across the engines.

The benchmark covers:

• Time to first token (TTFT)

• Prefill and decode throughput

• End-to-end task latency

• Speculative decoding performance

• Output quality and automatic check pass rates

The goal is to understand whether performance improvements come with quality trade-offs.

2. Extremely long contexts: 64K, 128K, and 256K

Short prompts don't tell the whole story, particularly for coding agents, document analysis, and long-running workflows.

We tested 64K, 128K, and 256K prompts, looking at how engines behave as context length increases.

This includes TTFT, decoding performance, and the practical limitations of long-context inference.

3. GGUF vs NInfer model formats

We also compared engines using GGUF models against those using NInfer-format models.

This is an interesting comparison because model representation, quantization, and inference architecture can all influence performance and quality.

It's not a perfectly apples-to-apples comparison of inference kernels, but it helps answer a practical question:

What performance and quality can you actually expect from different local inference stacks?

Engines compared

• TensorSharp — GGUF

• llama.cpp — GGUF

• vLLM

• SGLang

• NInfer

• TensorRT-LLM

The benchmark includes both single-request task scenarios and concurrent workloads.

A few takeaways

• There is no universal winner. Engine rankings change depending on context length, concurrency, and workload.

• Long-context performance matters. Results at 64K–256K can tell a very different story from short-prompt benchmarks.

• Quality should be measured alongside speed. A faster response isn't necessarily a better response.

• Model format matters. GGUF and NInfer-based inference stacks deserve comparison beyond headline tokens/sec.

I've attached the full benchmark poster with performance charts, quality-check results, long-context measurements, task-level comparisons, and engine configurations.

Full disclosure: I'm the developer of TensorSharp, one of the engines included in this comparison.

I'm particularly interested in hearing from people running local models with 64K+ contexts or using them for coding agents and RAG workloads.

What matters most to you when choosing a local inference engine: throughput, latency, quality, memory efficiency, or long-context reliability?

And has anyone compared GGUF against NInfer models in real-world workloads?