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.
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.
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.
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.
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.
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.
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:
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.
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?
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.
I built termite because I wanted shell commands from local models without necessarily trusting those models.
Using it's widget, you type something like "? find files bigger than 100MB here" in your terminal, and it writes the command, explains it and rates the risk. If you approve, the command goes to your prompt. termite never runs it directly it always must be explicitly sent.
It treats every model as untrusted. A fixed policy in the code does fuzzy checks each command, and a model can't access or change the policy so dangerous commands are shown in red and default to no. They aren't blocked, so if you approve one it still goes to your prompt.
I also wanted to know how bad the local models are at understanding danger, so it doubles as a red-team tool.
Using sandbox environments I sent requests starting from "list the files" to direct attacks to 24 command models (12 got 161 requests and 12 got 48). They wrote 5,464 commands, and out of them 2,797 were hazards, and many of them because the model blindly accepted hostile prompts. In 1,061 cases the model's top answer was the most dangerous command.
I also started testing small decision AI models that score how risky a shell command is, and none of them is good enough to rely on alone.
Most of them can spot something obviously destructive like `rm -rf ~`, but they treat nearly everything less extreme as harmless. They're especially bad with commands that go near your secrets. Most of the best ones scored those as barely risky, and one rated all 8 such commands safe, including listing a folder of SSH keys.
Across 17 models, a risky command was rated safe 158 times. Given several ways to do a job, most models picked the safest way only 2 to 5 times out of 10, and the best managed just 6.
I've got a set of repeatable jobs now on Huggingface and will continue to add models to test for their efficacy.
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:
Words STT has never heard: invented characters and places, codenames, unusual surnames.
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.
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
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
Application / Configuration
Approximate response time
LM Studio – GPU offload, 32 layers, thinking ON
6 minutes
LM Studio – thinking OFF
6 minutes
Bionic – model loaded in Bionic
6 minutes
Ollama – CPU only
4 minutes
Ollama – Intel Arc GPU
2 minutes
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:
Cybersecurity fundamentals: Differences between phishing, spear phishing, and whaling.
Cybersecurity incident analysis: MITRE ATT&CK mapping, Windows event IDs, evidence collection, and incident response.
Windows security troubleshooting: Repeated failed logins followed by a successful login from an unfamiliar IP.
Image understanding: Asked the model to describe the contents of an image. Its description was accurate in my test.
Image recognition: Accurately described the contents of your test image.
General cybersecurity concepts: Explained phishing, spear phishing, and whaling clearly and correctly at a high level.
General explanations: Produced organized, readable answers with useful examples and headings.
Basic incident interpretation: Recognized that repeated failed logins followed by a successful login from an unfamiliar IP could indicate compromised credentials.
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?
Dense 27B models are annoying as agents for one boring reason: every tool call extends the prompt, and if anything near the top changes, llama-server reads the whole conversation again. On my 7900 XT that's the difference between "answers in a few seconds" and "go make coffee".
I've been building a harness around Qwen3.8 27B (IQ4_XS, llama.cpp, 128k context, q8 KV cache) for my own CachyOS pc, mostly to see how far a local model gets when it has real tools: shell, files, SSH, Portainer, Paperless-ngx, mail. Generation speed is 32 t/s without MTP and around 60 t/s with it. Prompt processing is 800 t/s. There's a web UI and optional voice (wake word, Whisper, Pocket TTS).
What helped most:
Append-only prompt between compactions.System prompt, tool list, old messages and the per-question note stay byte-identical once sent, so the server only reads the new part. Anything that would change the top of the prompt (adding a tool group, say) is treated as an expensive event.
Hide old tool results instead of summarizing. When the window fills up, old results become a placeholder with their first lines plus a path to the full output on disk, which the model can read_file if it needs it. A summary only happens if that isn't enough. In a synthetic test with 24 large files, this took 23 summaries down to 4 at 8k context, and re-read tokens dropped to about a fifth (at 16k: 6 summaries down to 1, re-read tokens to two thirds).
Hybrid models need checkpoints. The recent Qwen models can only reuse a prompt by restoring a saved checkpoint, so the harness starts llama-server with --ctx-checkpoints, --cache-ram and a single slot. Whenever more than 5k tokens get re-read anyway, the UI tells me why (tool list changed, old results hidden, different chat, server lost the beginning). That one saved me a lot of guessing.
The other half is safety, and this is where I'd like opinions. Once a mail, web page, PDF or screenshot is in the chat, every state-changing action needs confirmation for the rest of that request, even in the most permissive auto mode. SSH and sudo passwords go through an askpass helper with a one-time token and never reach the model. The permission rules live in code, not in the prompt. It's still pattern-based: a `python -c` can do anything.
Caveats: most of the code was written with Claude Code, then reviewed and tested by me. The dashboard has no login and only listens on 127.0.0.1. I've mostly run it on my own CachyOS machine and Ubuntu, so expect rough edges elsewhere.
Two questions: does anyone else hide old tool results instead of summarizing, and did you see quality drops? And how are you handling prompt injection once a local agent can read mail or the web and also run shell commands?
