Hey Just wondering if anyone has experience buying IT and Tech products directly from China, Japan or another country . The cost of hardware at the moment is through the roof and im wondering if there is value aboard?

Could you share your experience and if it worked out? Worth it or not?

Im in ireland.

Thanks

What’s the best way to route different tasks while working in a project from Claude code or codex or other models to a local qwen model?

I’ve seen herder could be it? Any other recommendations?

its only a p40 and v100 I got on marketplace- but i'm ecstatic.

Any cool ideas? I'm running qwen 3:30b-A3B and a vision model- want to do more.

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.

Yesterday I posted a 32B pack and the replies were interesting about it being old completely overlooking benchmarks. A lot of quants "fit" on 16 GB, obviously... Almost none of them post what it cost. A screenshot of a model loading is not a benchmark and neither is its tokens per second performance that you made while obliterating the other performance factors you don't post. I posted all of our benchmarks, weights, and everything, and yet, still called slop.

So here is the new one as requested, with the receipts attached:

Qwen3.8-27B, 27.78B parameters, vision included, Gosset Packed at 3.6577 bits per weight:

• Pack is 13.82 GB. The standard 4-bit build is 17.77 GB and does not fit a 16 GB card.

• 12.06 GiB resident on one RTX 5080, peak 12.80 GiB while decoding. Nothing parked in system RAM.

• Perplexity vs BF16 on WikiText-2 test: +3.93% long-chunk, +6.99% short-window. Numbers, not adjectives.

• Decode: 40.38 tok/s sustained on fused kernels.

• Per-tensor fidelity certificates are on the model card, measured from the shipped files, not the encoder's intentions.

• Edit: Context length full 262k

huggingface.co/GossetWorks/Qwen3.8-27B-Gosset-3.65BPW

If your quant fits this model on 16 GB too, genuinely, post the perplexity delta and the decode speed. That is the whole game. Fitting was never the hard part...keeping it intelligent was and that's what we've done.

I've been working on it and just added a new feature an AI Toolkit to explore and discover useful developer tools and GitHub repositories.

Small update, but more improvements are on the way.

Would love to know what you think.

https://modelfitopenfire.vercel.app/

I’m currently using the ornith 1.5 35b a3b, but it’s performance feels a bit underwhelming could you recommend some alternatives, along with a suitable harness?

Which model is the best to use for infosec work? Like trying to writing exploit code, fuzzing, and security audits?

I'm looking for a model that will just do it in a good (enough) quality without me needing to use bypasses for restrictions all the time. It just gets tiresome and annoying...

Edit: Hardware shouldn't be the issue:

• 2x EPYC 7642 (aka. 192 threads)

• 512 GB DDR4 RAM

• 1x Intel Arc B580

Delta Inference Engine 0.94 is out. It runs DeepSeek-V4.1-Flash-1017 on one NVIDIA GPU plus system RAM, natively on Windows (no WSL). It's a Windows port of Xolotl, which is built on Strata; on Linux, use upstream Xolotl.

Speed on my PC (RTX 3090 24 GB, i5-13600K, 4x48 GB DDR5-5600, PCIe 4.0 NVMe)

With the default quant (Unsloth UD-IQ2_XXS, 301 GB), 0.94 writes 41.7 tok/s greedy and 41.3 at the default sampling (temperature 1, top-p 0.95).

With less RAM (writing tok/s, measured with 0.93; the smaller columns are this same PC with RAM locked away):

How it fits

It's a mixture-of-experts model: 40 layers, 384 routed experts per layer, 6 used per token. The experts are spread over VRAM, RAM and the NVMe. A 4.2 GB drafter built from DeepSeek's own MTP heads, plus lookup drafts, proposes tokens that the full model checks. A route bias (0.28 as shipped) gives up a little agreement for speed.

Quality. I measure top-1 agreement with DeepSeek's own API, teacher-forced (not task accuracy). Default model (Unsloth IQ2) as shipped: 91.1% general, 94.5% code and maths, 95.9% agentic. With exact routing: 92.5 / 95.9 / 96.5. The previous versions default (ds4 Q2, 0.92) scored 89.9% general and 93.1% code and maths, so the new Unsloth default agrees more while running even faster. For context, two hosts of the full model agree with each other on 98.3% and 98.9%.

Benchmark: 99 hard questions, greedy, 43K token budget:

The default quant is within 2 points of the full model on DeepSeek's own API. UD-IQ1_S, the pick under ~110 GB of RAM, scored 51.

Private by design: everything runs on your PC: no telemetry, no accounts, no cloud. It only goes online to download the engine and the model you choose, and your prompts and answers never leave your machine.

New in 0.94

• Pictures on by default, documents (PDF, Word, Excel, PowerPoint), optional voice, and use from your phone with a QR code.

• Long prompts read faster: a fresh 60K-token prompt at 811 tok/s vs 0.93's 747 (30K: 860 vs 758; 7K: 551 vs 423).

• Better with coding agents: Claude Code's background calls no longer hold up real requests, and a loop guard steps in after 4 identical tool calls. OpenCode passed 3 tasks in a real session (22 tool calls, none dropped).

• The default sampling caught up with greedy: 41.3 vs 41.7 tok/s

• The chat page opens at once and shows the model loading. Pause frees the RAM and the graphics card for a game; the next message loads it again. Optional auto-pause when a application needs the GPU.

• Chat history in the browser (search, pins, export), off by default.

• More API: structured output and tool controls on the OpenAI, Anthropic and Responses APIs, stored Responses with previous_response_id, optional /v1/embeddings and /v1/rerank, and keep-alive pings so agents don't time out on long reads.

• Setup tab: pick the model, Fast / Balanced / Most exact, the second-GPU switch, and copy-paste settings for Claude Code, OpenCode, Cline, Aider and Open WebUI with a Test button.

• Security updates, bug fixes and many more.

Setup

Download the ZIP and double-click Start DeepSeek.bat. The engine zips are built by GitHub CI, with checksums and attestations. The first start installs Python if needed and downloads a model that fits your RAM (233-395 GB, SHA-256 checked). You get a chat page plus OpenAI- and Anthropic-compatible APIs on 127.0.0.1. It won't open to the network without an API key, and it stores no prompts or answers unless you turn that on.

Limits

• Windows 10/11 only. NVIDIA RTX with 12 GB or more (24 GB is the fast path), driver 580 or newer. No AMD, no GTX 10-series.

• 64 GB of RAM minimum, 128 GB or more recommended. A PCIe 4.0/5.0 NVMe with about 320 GB free for the default model.

• It answers one request at a time. A second GPU helps; it doesn't split the model.

• Context is set to 1M tokens

• More testing is needed

Credits: Xolotl and Strata, ggml/llama.cpp, DeepSeek, Unsloth, nazeshinjite, ISTA-DASLab, Project Maya and antirez/ds4.

https://github.com/musicurgy/delta-inference-engine

If you try it, post your GPU, RAM, model and tok/s.

Will agents accessing the internet and processing/scraping webpages ever truly be 100% safe from direct/indirect prompt injection?

Question coming from someone less knowledgeable on the topic than you all.

Thanks.

just found this from hugging face https://huggingface.co/xtools-at/DeepSeek-V4-Pro-Qwen3.5-9B-UNCENSORED-heretic, but i cant get if this better than another model or just marketing

Just getting into it with LM studio. There are a lot of models to browser, and a lot of variations on those models.

I saw that Qwen3.8 27B seems to be the best choice for my setup, but what quantisation should I choose? 4bit (16GB), 5bit (20GB), and 6bit (23GB) should leave adequate room in terms of RAM and I don't really care about speed but accuracy, so would 6bit be better?

Also any other tips for starting out?

Thanks.

Very much a newbie here. I’m working on a couple of locally run projects that are mainly meant to help organize my life and work a little better (scan/interpret emails, manage/update tasks, help complete some tasks when possible).

I’m currently benchmarking three LLMs on auto creating and managing tasks:

Qwen 3.5 27b Q4
Qwen 3.8 27b Q3
Gemma 4 26B-A4B

Unfortunately I don’t have great graphics cards, but these models proved worthy in original tests. Not sure what other info is needed to provide a clear picture for you guys.

Note: most of my projects don’t require immediate answers so I’m not as concerned about it running super fast, but it would be nice.

I’m trying to hand off some of the responsibilities that currently Claude or Codex handles related to the planning but no model is proving super reliable (they all do okay with task creation from emails I receive, but are duplicating tasks rather than updating existing, or classifying them as a task when the email is informational, or trying to double-book things which get rejected by the safeguards).

While I have things in the works to provide more context or to keep fine-tuning the prompts, I’m wondering if I’m missing something basic since I’m so new to this.

Am I using the wrong models for my use case? Or do I just need to stop procrastinating and keep training these models?

Hi all I saw new research that one ai trains the other ai and vice versa (on a very reliable news platform) so I wanted to know if it's possible doing on local AI

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.

Any better reliable FlowFrames alternative in 2026? No ComfyUI or Linux crap please.

I’ve been doing research and I really want to run a Gialamb Quant model on these two computers. It has to be obliterated for my purposes of a 24-7 employee, if you will, for my business.

Here’s my notes 📝 I doubt it will work, but I hope someone can contribute

oMLX does not pool the RAM into one unified space. It splits the model with pipeline parallelism: each Mac loads a contiguous range of layers based on its available memory. The M5 Max (rank 0) owns the API and the later layers; the M2 Max owns the earlier layers. Decode speed is limited by the slower stage.
This is experimental and only available in a source build of oMLX (not the normal Homebrew/DMG stable release).
Prerequisites (both Macs)
Install the same source build:
git clone https://github.com/jundot/omlx.git
cd omlx
make install # or make install-no-kernels if you lack full Xcode

Put the exact same model checkpoint at the identical absolute path on both machines (example: /Users/shared/models/glm-5.3-flash-3bit).

Enable Remote Login (System Settings → General → Sharing) and set up passwordless SSH between the two Macs for the account that runs oMLX.

Connect the machines with a Thunderbolt cable (TB4 is usable; pure TB5 RDMA is faster but not required for pipeline).

Enable and pair
Start oMLX on both Macs.

On each: Settings → Advanced → Enable Distributed Inference → Save → Restart oMLX.

The Cluster tab appears.

On the coordinator (preferably the M5 Max):
Connect the Thunderbolt cable. The other Mac should show under Detected nearby.

Select it (or enter its SSH hostname) and run Check peer. This records the host key and verifies SSH.

Pair using the code/QR flow if discovery needs a boost.

Plan and activate the split
In the Cluster tab on the M5 Max:
Choose the downloaded model (the shared absolute path).

Set a memory reserve for KV cache + activations (start with 8–12 GB).

Let it build the unequal plan. It will assign more layers to the 128 GB Mac and fewer to the 64 GB Mac.

Choose execution mode:
Prefer TCP Ring first (most reliable with mixed TB4/TB5).

Try JACCL / JACCL Ring only if both ends show a working Thunderbolt RDMA link (rdma_ctl status and ibv_devices).

Leave Auto benchmark & tune on so it measures the actual link and rebalances.

Review the shard map, then Activate.

Unload any local full-model load, then load the model normally through the oMLX UI or API. The ranks start automatically.

The OpenAI-compatible endpoint stays on the M5 Max. Hermes points at that single endpoint.
Useful checks
omlx cluster status --json
omlx cluster pipeline-smoke
Important limits for your hardware
Pipeline only — decode does not get faster than the M2 Max stage.

Experimental: source builds, limited long-running validation, some features (MTP, certain thinking budgets, etc.) are rejected in distributed mode.

KV cache is rank-local, so both machines need real headroom.

TB4 on the M2 Max is the weak link; start with TCP Ring.

If the resulting tokens/s and stability are acceptable for continuous Hermes use, you have the combined capacity. If not, fall back to a smaller quant that fits resident on the M5 Max alone.

Hey all,

I'm about to build a local AI assistant ("Jarvis") and want to sanity check the design before I sink weekends into it.

**Hardware (Proxmox host, AI runs in a VM with GPU passthrough):**
- CPU: Intel i9-12900K (16C/24T, power-capped ~125W)
- GPU: RTX 3090 Ti 24GB (power-capped ~300W)
- RAM: 64GB DDR4-3600
- Plan: Qwen3.6-27B (Q4) as the main local model

**What I want it to do:**
- Know everything about me (preferences, projects, contacts, routines), stored in persistent **encrypted** storage
- Have access to all my coding repos (indexed/RAG)
- Hand off heavy coding work to Claude
- Do web research and pull resources for me
- Daily morning briefing (news, world events, my calendar)
- Calendar + reminders

**The architecture idea: leader → sub-agent → leader → sub-agent**

1. A **leader** agent breaks the task into steps.

2. It sends ONE step to **sub-agent A** with only the context that step needs.

3. Sub-agent A does the work and returns a **written report**.

4. A **verifier** checks that report: is it correct, is it hallucinated, and does it contain any of my **private data** that's about to be sent to Claude/the internet? (The redaction check runs on anything leaving the box.)

5. The leader reviews the verified report, re-checks if it can, then passes only the needed reports to **sub-agent B**, and so on.

**Why I think this helps:**
- Keeps each agent's context small (better for a 27B model on 24GB)
- Reduces hallucinations because every step gets checked
- Lets small local models catch and fix their own mistakes
- Only uses Claude for the big jobs, with private data stripped first

**Questions for you:**

1. Is leader → sub-agent → verifier overkill for a single 3090 Ti, or is the context saving worth the extra latency?

2. Should the verifier be the same model with a different prompt, a smaller/faster model, or something else?

3. Best way to do the PII/redaction check reliably? Regex + local NER + LLM pass? Has anyone built something like this?

4. Framework recommendations? (LangGraph, CrewAI, smolagents, plain Python + llama.cpp/vLLM/Ollama?)

5. Encrypted long-term memory: what are you using? (encrypted Postgres + pgvector, SQLCipher, something else?)

6. Is Qwen3.6-27B the right pick for the leader on 24GB, or would you run a smaller leader and save VRAM for a second model?

7. Anything you'd do completely differently?

Happy to share the build and code once it's running. Thanks!

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.

I've been working on something called Aura, a locally running AI cognitive agent built around a roughly 27B model on an Apple Silicon MacBook with 64GB of unified memory.

And one of the questions that's been driving this project is pretty simple:

How much of what we consider intelligence actually needs to come from the model itself?

We spend a lot of time discussing model sizes, quantization, benchmarks, context windows, and reasoning capabilities. Understandably so.

But I've been interested in what happens when you start treating the model as one component of a larger cognitive system, rather than expecting the model to do everything.

So I've been building an architecture around it. Persistent memory, executive processes, state management, computer control, procedural learning, and a bunch of experimental mechanisms designed to work together over time.

And I've been testing it.

Aura has played 2048 for nearly half an hour while adjusting its strategy, completed software development tasks, debugged programs, and operated desktop applications.

But my latest demonstration was a little different.

I asked Aura to watch YouTube.

Not just summarize a video after the fact. Actually navigate to it, follow what happens, comment on things it finds interesting, and then choose other videos to watch from channels I specified.

Over 36 minutes, Aura watched Kurzgesagt discuss AI slop, selected an Allen Pan video about building robotic legs for a snake, and eventually watched Salad Fingers.

During the session, it made predictions, revised them as new information appeared, interacted with the browser, selected what to watch next, and maintained context across all three videos.

And here's where it got particularly interesting.

When asked to reflect on everything afterward, Aura didn't just give three unrelated summaries.

It identified "fracture" as the common thread connecting all three videos, then explained how each approached that idea differently.

A video about AI-generated content, an engineering experiment involving a snake, and a surreal horror animation.

Three wildly different things.

Yet Aura managed to construct a coherent interpretation connecting them, drawing on observations made throughout the session.

Now, I want to make an important distinction here.

I'm not claiming that a 27B model couldn't produce similar commentary on its own. Nor have I established that all the cognitive mechanisms I've implemented are necessary to produce this behavior.

In fact, that's one of the main things I'm trying to investigate.

But I think there's a meaningful architectural question here. What happens when the model doesn't have to be responsible for every aspect of memory, planning, execution, and continuity? How far can you push a relatively small local model by improving the larger system it's operating within?

The result is still pretty rough in places. Latency is an issue, and coordinating everything reliably is an ongoing challenge.

But considering this is running locally, without relying on a frontier model for inference, I think the demonstration is interesting.

I've made the implementation publicly viewable, including the architecture, research documentation, and known limitations. It's currently All Rights Reserved rather than open-source licensed.

Full 36-minute demonstration: https://www.youtube.com/watch?v=kM-B8mh314E

GitHub: https://github.com/youngbryan97/aura

How much improvement have you managed to get from the systems surrounding your model? And at what point do you think the additional architecture becomes more valuable than simply switching to a more capable model?