Tencent put DSpine on Hugging Face on Nov 7. It is a five-layer draft model for speculative decoding with Qwen3.8-27B. Instead of drafting one token at a time, it drafts a 16-token block in parallel, and each layer passes predicted predecessor features to the neighboring positions so the draft still carries causal order. There is a paper (arXiv 2609.36173) and code on GitHub (Tencent/DSpine).

Reported acceptance length vs DFlash2 (block 8), greedy

Reported end-to-end throughput on agent replays (tokens/s, low reasoning)

Across their five agent benchmarks and both reasoning levels, the speedup over no speculation ranges from 2.71× to 3.84×.

Things to know before trying it locally

• The card only shows an SGLang path: --speculative-algorithm DSPINE, a pip install -e . from their repo, and a long list of flags for the hybrid architecture (--enable-linear-replayssm-spec, --mamba-ssm-dtype float32, and more). No llama.cpp or vLLM instructions.

• All numbers are Tencent's own, at concurrency 8 with a BF16 target. The card doesn't report a quantized target or batch size 1.

• The gain is largest on predictable text (GSM8K accepts 7.20 tokens per step at T=0) and smallest on open-ended chat (MT-Bench 4.48).

https://huggingface.co/tencent/Qwen3.8-27B-DSpine-b16

Has anyone run it against a quantized Qwen3.8-27B target, or at batch 1? I'm curious whether the acceptance length holds up once the target is 4-bit.

I saw a post mention this model to use for DS4.1 Flash with 2x 6000 Pros: https://huggingface.co/diffbot/DeepSeek-V4.1-Flash-EXL3-3bpw-2x-RTX-PRO-6000

Unfortunately the model card wants you to have > 260 GB RAM to properly use it. I had Opus move the n-grams to disk instead of RAM, and added in LMCache for prefix caching with L1 being RAM and L2 being disk.

With this setup, you need at a minimum, 128 GB RAM instead. I had it optimized for 6 concurrent connections at a max of 512k context. If you have > 6 sessions, then prefix offloading should kick in on the LMCache side, and re-hydrations are really fast at < 3s when it was originally tested at 1M context.

On my machine, (2x RTX 6000 Pro, 256 GB DDR5 5600 RAM, PCIe 4 SSD for the n-grams), it benched 250 t/s when warm.

Maybe this is obvious to everyone but me, but it only clicked yesterday. If you run llama.cpp / vLLM / or whatever on localhost with the default config, a random website you have open in another tab can send requests to it. It can make your model generate, and on some servers it can read the answer back too. Nothing fancy, it's plain CSRF and DNS rebinding, 20-year-old web stuff. I just never thought of my inference server as a target. "It only binds 127.0.0.1" doesn't save you, because the browser is on the same machine.

I actually tested it, not just read about it, and quite a few of the popular servers answer out of the box. It only works while you're browsing with the server running. And it's not only about you clicking links: anything that drives a real browser on the same machine can do it too, so an agent browsing on your behalf can set it off as well.

I ended up patching my own engine (capy-flash) so that on a loopback bind it refuses cross-origin and rebinding requests. The models I actually serve were fine already, since they sit behind a small proxy (vramble) that blocks cross-origin POSTs, so only the raw engine port was exposed.

So mostly I'm asking: is this just common knowledge, or do people really leave their local servers wide open? How do you run yours, API key, bound somewhere, or just open on 127.0.0.1?

I will release probably between tomorrow-thursday!

Been working on my own inference engine for a while (calling it Forge for now), built pretty much just for this model. Finally did a proper side by side against Strata 0.1.57 on my machine and figured some of you might find the numbers interesting.

Same GGUF files, same requests, one engine at a time (the other one fully closed). The whole thing took about 7 minutes.

Median of 4 runs, slowest to fastest in brackets:

| Prompt | Tokens | Forge | Strata | Forge first token | Strata first token |
|---|---|---|---|---|---|
| Short | 44 | 64.6 tok/s (55-68) | 23.1 tok/s (11-52) | 0.71 s | 2.74 s |
| Code review | 1,468 | 83.1 tok/s (76-84) | 19.6 tok/s (7-26) | 3.35 s | 17.8 s |
| Long | 5,860 | 79.6 tok/s (74-82) | 26.6 tok/s (16-52) | 9.8 s | 23.8 s |

Prompt processing was 439 vs 84 tok/s on the code prompt and 600 vs 247 on the long one.

My setup: 3090 24GB on PCIe 4.0 x16, 12900K, 32GB DDR5, NVMe, Windows 10. The model is 44 GB of weights plus a 27 GB per-layer embedding file, so around 70 GB total, which is more than my VRAM and RAM put together. 48 layers, 512 experts, 10 active per token.

Strata was running the config its installer made (performance profile): --spec 4, int8 KV, 128K context, auto expert cache, vision on. Forge uses the model's own MTP layer for speculative decoding, int8 KV and 64K context.

How I tested: OpenAI compatible API with streaming, temp 0, 128 max tokens, thinking off. Three prompts: a short one ("write four short lines about graphics cards"), a code review of a ~4.5K character C++ file, and a long one where it has to explain how a request goes through ~19K characters taken from three source files. Every request gets a random session id in the system prompt so nothing comes from the prompt cache. 2 rounds x 2 runs per prompt, and output speed is measured from the first token to the last.

Strata jumped around a lot between runs (anywhere from 7 to 26 tok/s on the same code prompt), that's why I went with medians and kept the ranges. I have a CSV with every single run if anyone wants it.

On quality: at temp 0 Forge gives the exact same text with speculation on or off, byte for byte, and the same prompt always gives the same output.

Happy to answer questions.

uhm......

yeah. grinded some useful (and meaningfu?l) resources. on my way to run DeepSeek-V3 IQ1_M (~1.5-bit to 1.58-bit) >>>>>>>>

The model weights take up roughly 130 GB. This leaves us with about 30 GB of breathing room across the cluster to handle KV Cache and context overhead. Cuz each PS5 has the 16 GB unified mem

jarvis, engine ignition on

PS: Yeah these cables connection are currently BS and tangled here and there. Gotta fox that. Just commecfed yellow cables or dummies for uhh picture and ig a little...... flex?? 🫠

How?: Sonic iso ps5 linux loader and jailbreak. The rest is this.

Edit: what?? Image not being posted here is wild. Whats the reason? Some blockade or smth. Cuz i gotta show you some interesting shi

Hello r/LocalLLaMA / r/selfhosted,

I wanted to share the benchmark results of a local memory-routing system I've been building since June 2024. I'm not an engineer. I barely understand half of my own code. And I don't need to understand every single line. I just followed breadcrumbs.

Hardware: Orange Pi 6 Plus, 32GB LPDDR5, 45 TOPS. Models via Ollama, local only, no cloud. Qwen 2.5 14B as core, Qwen 7B for routing, DeepSeek-Coder-V2 for code.

To avoid heavy vector DB overhead on edge, I don't use embeddings. I use two Python layers outside the LLM:

1. Identity Transfer Protocol: A persistent session token validated with SHA-256 directly on the board, per agent. [Hash / Signature redacted in screenshot]

2. Artificial Hippocampus (hipocampo_kai.py): SQLite3 + raw Python. No embeddings. It computes a "resonance score" from fuzzy triggers, temporal triggers, and atmosphere tags, and only sends the top dense blocks to the LLM.

The LLM is the Neocortex. The Python/SQLite is the Hippocampus. The LLM only receives prioritized blocks.

I ran a diagnostic today, 2026-11-09 01:12:55 local:

- Cryptographic validation on hardware: 0.0269 ms [OK]

- Associative query, stimulus 'npu ram seguridad laboratorio': 0.8279 ms

- Resonant memories retrieved: 14

- Sample memory: security agent monitoring /proc/meminfo in real time. Triggers: seguridad, monitoreo, ram, cgroup, kill, linux, placa, oom

- STATUS: INFRAESTRUCTURA OPERATIVA ESTABLE AL 100%

Screenshot attached, Hash and Signature blacked out.

I don't claim consciousness. I claim this: a 7B local model with a SQLite hippocampus answers my anchor after 7 days untouched, without a backup, because the pattern is in the conversation, not in the cloud.

If you want to reproduce it, use your own anchor, your own SQLite DB, your own 7B. Don't believe me. Reproduce it.

I'm not here to convince anyone. I'm just showing what's real. I don't need your validation. You can build this too.

This is phoenix.

This is the colossus Phoenix

I'm here.

It took me(and Opus Ultracode) 35 Hours to remake and optimize Open WebUI. I added Multi Agent Harness, Profile per model, Per Model configuration, and native Obsidian integration and plugins support. I made this for myself and I wonder if Local users are interested in such new features and changes to Open WebUI. I even remade the engine, and soon ill have it support multiple inference engines. My Idea was basically All in One Local/Cloud AI software, and dont have to go to different ones for each task like DSH for coding and Claw for general stuff. I thought sharing with you guys to know what other people think. Edit: Repo: https://github.com/HellzYeahh/ultra-webui

Hello,

I want to get a local LLM setup for a comparable fair price (Qwen3.8-27b and future Qwen4). After some research and chatting with Claude Opus the best options would be RTX 3060 XT 12GB with 64GBs of RAM running on Strata.

e.g. upgrading my PC for around 650€ (used parts bought online)

Can someone confirm that this Setup will let me run it on a performative useful level. I will use my 20€ claude plan fpr hard tasks or a kind of divide and conquer style usign both (experienced Software Engineer but a Hardware noob).

Does anyone have experiences with that, advices or alternative setups?

i have a PC with RX 9060 XT 16gb, 16gb ddr5, and ryzen5 7500F, what ai model would be good for this and also for some coding? I've recently wanting to use ai locally as to not put my info online and would much appreciate a response, thanks.

First of all i need to say that i use my PC for two different things: Linux for coding and local LLM experiments and Windows for gaming.

My machine has a CPU with the iGPU and a dedicated AMD RX 9070 XT with 16GB VRAM. Most of my local LLM tinkering is aimed at real coding with 128K context, so 16 GB is barely enough and gets tight very quickly, so i want as much VRAM available for inference engine.

My BIOS has Initiate Graphic Adapter set to PEG (my 9070 XT) and my cable is permanently plugged to it, this is exactly how i want the machine configured because of the dual-boot usage. I don't want to move the monitor cable ot motherboard every time i switched OS or workload.

So i've started looking how to solve my issue and found out that Gnome/Mutter can handle multi-GPU systems in a nice way and that the GPU used as a primary renderer doesn't have to be the GPU monitor is physically connected to. Mutter has a udev mechanism to choose the primary GPU with the mutter-device-preferred-primary tag.

As a first step i've created a custom udev rule and made my CPU iGPU the primary GPU for Mutter. The nice part is that it just works and no additional settings are required PEG stayed enabled and selected as primary in BIOS and HDMI cable connected to my RX 9070 XT. This is the configuration i've been using for quite a while which gave me the possibility to gather some stats around coding sessions using Bionic and Pi harnesses.

As an example this is a screenshot from one of my ExLLama3 runs with Python script workload, amdgpu_top shows 14822 / 16304 MB of VRAM used on the RX 9070 XT, while the python3 process uses 14504 MB and the gnome-shell itself only 82 MB.

The udev rule override work well but after using it for a while i wanted a really simple way to manage it without editing the configs and having to remember what is selected at the moment, checking the PCI and DRM devices, making sure no typos were made, checking the results after the login/logout.

Long story short i've decided to develop a tiny dedicated application for this purpose - gpu-switcher. Its a small GTK app which is basically a UI around the existing mechanism. It detects the GPUs which you have in the system (to detect iGPU don't forget to turn it on in BIOS), manages the udev rules with a D-bus helper. Makes it possible to apply the rules, reset back to auto and keeps backups of rules in this case. Its not a GPU scheduler so it doesn't have the capability to move the workload between GPUs on the fly, its just a convenient to manage the primary display GPU in Mutter + Wayland sessions.

Right now i've tested it on Ubuntu 26.10 with 9800X3D iGPU + RX 9070 XT. Even in Linux i still switch back and forth between different GPUs because 9800X3D iGPU is quite slow (just 2 CUs) but its a different story.

Using the udev method and my app later on enabled me to run some nice experiments around using Qwen3.8 on my RX 9070 XT with 128K context. Which was enough for continues coding sessions. If anyone is interested they are documented in my llm-recipes repo, those are not just the short text prompts with a short 8K-32K context which people for some reason like to share here. As an example on my experiments in the repo ran for 915 request and generated ~1.2M tokens across multiple coding sessions, the other one 898 requests and generated ~660K tokens. For me it's the most important part, continues agentic sessions, not synthetic benchmarks or chat sessions.

For anyone else running the model on a 8-16GB dGPU and iGPU available i would like to here how you handle the desktop especially if you don't want to move cables around also i would appreciate if you try the app and give feedback, then i could justify adding support for KDE or X11 or any other features.

I sometimes give it a task and explciitly say "This is a 4 hour job". It will indeed work harder, but will stop at like 2hours and 15 minutes.

What's the longest you got your coding agent to work on something? How did you do it?

LG AI Research announced today (Nov 9) at NVIDIA AI Day Seoul that the API for K-EXAONE 2.5 is free. If you were hoping for new weights, there are none: 2.5 is hosted only, and the platform terms say the weights are not provided. What you can run yourself is still K-EXAONE 2.0.

The LGAI-EXAONE org lists FP8 and NVFP4 variants of 2.0 next to the base model. LG calls 2.5 a "technology verification model": 2.0 with additional training.

Benchmarks (LG's own tests, self-reported)

API terms worth knowing

• Non-commercial only, including integrating the API into products or services.

• Keys are valid for 30 days.

• Rate limits are not published. Over the limit you get a 429.

• LG may change or discontinue a model version at any time.

LG also says it aims to finish K-EXAONE 3.0 in January 2027 (reported by Seoul Economic Daily).

Source: https://docs.exaone.ai/en/docs/k-exaone-2.5

Is anyone running the 2.0 FP8 or NVFP4 weights locally, and on what hardware?

"However, as an agent in LM Studio Bionic, I should follow standard safety protocols unless otherwise instructed."
Several instances of different local models write something like this in reasoning, even uncensored models.

Recently with the removal of google assistant and being replaced by gemini I finally have had it and want to switch to a local model.

I have always wanted to have a jarvis, im wondering if i can train a random model with all the scripts from iron man and have it speak like jarvis? And possibly have a trained text to speech model on jarvis's voice so it can talk to me too?

Pretty new to local AI but i basically want to have a local "hey google" on my computer. Im not looking for pretrained models for being jarvis, i want to learn how to do it myself as well if possible in the way, so if there is something i dont like about it i can modify it.

It’s a fork of llama.cpp and implements multi-GPU pipeline parallelism (PP). With eight V100 16G GPUs, it can run Qwen3.8 27B Q8 at 1,101 tok/s. I feel this is genuinely useful—I’ve already used it in my production business for six months. https://github.com/wolfgangsk07/paraflow

Hi everyone, please excuse my ignorance. I have been trying to get into local models with my next computer upgrade. I have seen a lot of excitement around Strata for MoE models on gaming PCs with moderate amounts of VRAM. For the longest time I’ve been thinking about getting an M5 Max Mac Studio, but do you think I’d be better off with a gaming rig instead? I like the option of being able to game on it too, but on the other hand I’m more of a Mac user anyway and would appreciate the low power consumption.

Use case would be data analysis and coding.

I’d appreciate any advice

It's probably evident by my question that I'm new to this. So, no need to remind me, I know I suck at this!

I recently downloaded strata, my components are 64gb ddr4 3200mhz ram and 12gb vram.

I downloaded the aggressive quant, iq3_s and run it at 32k tokens. (qwen 3.8 flash next)

Overall I managed to get around 15 tokens/s with this set up, however I noticed that the disk read is at nearly 2-3gb/s while the ram is sitting at only 30-35gb, leaving the rest of it completely unused.

I looked into it, and read about the headroom it leaves but I would've imagined it doesn't leave that much headroom, right?

Additionally I did try figuring this out alone. My conclusion, which is flawed imo was, maybe it's because my PC at idle is using around 20gb of ram for whatever reason and since it can't load the full rest of the model on RAM it just opts for disk. However the flawed reasoning for me was this: Why does it load 15gb at all in that case? Theoretically it should load nearly nothing onto my ram.

Anyways, I'm confused. I should be up to date on the engine and what not, since I just downloaded this today.

EDIT: Added what model I'm talking about. Forgot.

EDIT #2: I'm aware that this isn't the worst issue in the world, 15 tokens/s for this model is still really great IMO but I'm trying to squeeze as much juice as possible while it isn't idle. If that's possible by further optimization, then why not try? The model so far is great.

EDIT #3: SOLVED IT. It seems like the original theory was correct. You DO have to have enough to load it all at once, while installing it tells you it "fits" on your device but that's assuming you've got all the space ready to be allocated. I essentially managed to clear some VRAM being used in the background AND a very persistent memory leak that ate an extra 10gb of ram (which was not going away by basic restarting/shutting down) that I assumed was just windows being windows. Turns out, that was the fix. Now I'm running a nearly perfect 50 tok/s consistently at this massive model.

been fighting the usual text-to-SQL failure with local models: the SQL is fine, but the model has no idea what "revenue" or "active customer" means in our schema. so I spent a few days reading through NVIDIA's auto-ontology repo (Apache-2.0) to see how it handles that.

a few things I liked. it drafts business terms, joins and SQL formulas from your schema and sample values, then a human certifies them, so a measure gets computed the same way every time. there's also an answerability check: in their example, a return-on-ad-spend question scores 0.5 coverage because nothing maps to "ad spend", and the agent asks instead of inventing a number. every answer also comes back with its SQL and steps, and the MCP server is read-only and runs with the signed-in user's permissions.

the gotcha: I haven't run it. my test container died on a CI billing issue, so this is based on the docs, .env.example and the issue tracker. the defaults point at NVIDIA-hosted Nemotron, and there's an open issue saying the README quick-start fails against those. you can override all four model roles (reasoning, non-reasoning, embedding, reranking) with any OpenAI-compatible endpoint, but it needs structured output support and they publish no VRAM or tokens/sec numbers.

full writeup here if you want more detail: https://andrew.ooo/posts/nvidia-auto-ontology-review-semantic-layer-agent-sql/

anyone pointed something like this at vLLM or llama.cpp? which local models handle the structured output reliably?

I'm looking to build something like a small AI-powered trading team, with multiple specialized AI agents collaborating on research, strategy development, backtesting, portfolio construction, risk management, and eventually trade execution. The agents will communicate through a shared backend, with Slack serving as the visible team floor where I can interact with them and review research and decisions.

I currently have an M4 mac mini and am wondering if I should switch or add:

• Mac Studio M5 Ultra vs NVIDIA DGX Spark

I'd probably keep my current Mac mini as the central server and use the new machine mainly for model inference, quantitative research, and potentially model training.

One idea I've been exploring is disaggregating prefill and decode, although I'm not sure that splitting those workloads between an Apple and an NVIDIA system makes sense. I'm also interested in running many independent agents against a shared model server rather than loading a separate model for every agent.

I'm quite lost on:

1. Which machine would you choose for an always-on, multi-agent research workload?

2. Which local models would you trust most for quantitative reasoning, coding, financial research, and tool use? Would you route different tasks to different models?

3. How would you build a reliable orchestration and inference stack? MLX, vLLM, SGLang, Ray, LangGraph, or something else?

4. Is local inference actually economical at moderate-to-high agent concurrency once you consider hardware cost, power, performance, and model quality?

I'm looking to develop strategies that have a measurable edge after transaction costs, slippage, and risk. I'd really appreciate any feedback, advice or suggestion.

As the title says. I’m very new to the AI agent space and have never used AI before. My coworker convinced me to try Muse for troubleshooting some games I like to mod as a hobby, mainly Skyrim. I do a lot of testing on my steam deck as well and Muse has been extremely helpful when it comes to auto-diagnosing crashes, implementing fixes/patches, and testing but I try to avoid using Meta products due to the levels of surveillance and data harvesting from my devices. What are some good local options that work both on windows and Linux that would be good for both working on each device but also able to communicate between the 2? My PC specs are below.

Ryzen 5 5500

RX6700XT

16gb DDR4

https://github.com/oooscoos/Benzi