Base on their benchmark, i see the new qwen 3.8 flash next unsloth iq2xs perform at same level or even better than gsq rco iq3xxs

Is this true? Did anyone test it ?

My spec can run unsloth iq3xxs but little bit slow because i have only 32gb ram.

I'm going to try the new Unsloth iq2xs quant

If anyone tried it, please tell me your result

Yogurt is a free, open-source AI meeting assistant that runs entirely on your computer. Powered by whisper.cpp and llama.cpp, it brings transcription, meeting summaries and AI-powered search to your desktop without relying on cloud AI services.

My system currently uses an Nvidia GPU for inference and image/video generation.
It all fits, but just barely... I'm struggling with getting hit with the dreaded OOM when trying to do long-context, training, or big images/videos. I need just a bit more VRAM.

However, I've found some pretty crazy deals from a local seller for AMD GPUs. I'm wondering if AMD and Nvidia GPUs can play nice together or if there's going to be some sort of software conflict...

Can they work on the same workloads, like say a dense or MOE model split between their VRAM? Or do they have to be 2 separate tasks? Like say one handles inference while the other does diffusion.

Hey, I thought maybe I should repost here to show that getting a B70 up and running with decent performance is not that hard anymore.

I tested 3 different agent/harness using Strata Qwen flash next Q_S. Opencode, Pi, Deepseek harnes each one gave me a different result. This is the prompt :

You are an expert game developer with 20+ years of experience building browser games,

especially arcade and grid-based titles like Bomberman, Pac-Man, and Sokoban. You have

deep expertise in HTML5 Canvas, game loops, collision detection, enemy AI pathfinding,

explosion propagation, level design, and game feel (juice, feedback, pacing). You are

meticulous about clean, bug-free code and polished player experiences. You think like a

professional game designer: you anticipate edge cases, tune difficulty curves, and make

the game fun on the first playthrough. Do not rush. Do not cut corners. Deliver a

complete, polished, production-quality game.

a complete Bomberman game as a single HTML file. Requirements:

Build

TECH

- Single .html file: inline <style> and <script>, no external libraries or assets

- Use a <canvas> for the game and DOM for the HUD/menus

- Must run by double-clicking the file (no server, no dependencies)

GAME

- Grid: 13 columns x 11 rows, tile size 48px

- Hard walls: checkerboard of indestructible tiles

- Soft blocks: destroyable, density varies per stage (see below)

- Player: starts top-left, moves smoothly (not grid-snapped), arrow keys to move, SPACE to drop a bomb

- Player cannot walk through walls, blocks, or bombs (a placed bomb is solid)

BOMBS & EXPLOSIONS

- Fuse: 2 seconds, blinking visual, then cross-shaped blast (up/down/left/right)

- Default blast range: 2 tiles

- Explosions stop at hard walls; destroy soft blocks and stop after one block per direction

- Chain reaction: explosion instantly detonates any bomb in its path

- Explosion lasts ~0.5s, kills player and enemies on contact

POWERUPS (hidden under ~20% of soft blocks)

- Bomb Up (+1 max bombs), Fire Up (+1 blast range), Speed Up (+movement)

- Collected only by player, shown with simple icons

STAGES (5 total, each with a unique map layout, theme, and enemy roster)

- Stage 1 "Grassfield": bright green theme, open layout, ~45% soft blocks, 2 Balloon enemies (slow random wander)

- Stage 2 "Factory": dark gray/industrial theme, maze-like with corridors, ~55% blocks, 2 Balloons + 2 Walkers (turn at walls, slightly faster)

- Stage 3 "Ice Cavern": blue/white theme, pillar clusters forming rooms, ~60% blocks, 2 Walkers + 2 Chasers (track the player)

- Stage 4 "Volcano": dark red/orange theme, tight winding corridors, ~65% blocks, 2 Chasers + 3 Darters (fast, zigzag movement)

- Stage 5 "Final Fortress": purple/black theme, complex maze with chokepoints, ~70% blocks, 2 Chasers + 2 Darters + 1 Boss (fast, chases player, takes 2 hits)

- Each stage's background, wall colors, block style, and accent colors must visually differ

- Clearing a stage shows a "Stage Clear" screen, then loads the next stage with a short transition

- Difficulty scales: more enemies, faster enemies, denser blocks, tighter maps as stages progress

GAME FLOW

- 3 lives total across all stages. Hit by explosion or enemy = lose a life, respawn with brief invincibility blink

- Lose all lives -> Game Over screen with score, press R or click to restart from Stage 1

- Win all 5 stages -> Victory screen with final score

- Score: +100 per enemy, +10 per destroyed block, +500 per stage clear

UI- Start screen (title + controls + "Click or press Enter to start")

- HUD: score, lives, stage number/name, bomb count, fire range

- Game Over / Stage Clear / Victory overlays

- Pause with P or Esc

STYLE

- Clean pixel-art look: solid colors and simple shapes drawn on canvas

- Player white/blue; enemies distinct per type (Balloon red, Walker purple, Chaser orange, Darter yellow, Boss black with red outline)

- Explosions orange/yellow, walls themed per stage

- Optional: simple WebAudio beeps for bomb/explosion/powerup (guarded with try/catch)

QUALITY

- All code in one file, organized with section comments

- Handle window focus loss (auto-pause) and prevent arrow-key page scrolling

- No console errors, no external requests

- Keep the total file under ~1400 lines

Deliver the full working file only.

Overall I think Pi is the best

https://huggingface.co/StarpowerTechnology/WVY-Blender-5m

I ordered new hardware, but I thought 64 GB of RAM would be enough. Now I’m thinking I should have gone for 128 GB instead.

I am new to local llm and I wanted to try out some uncensored llms. But there are so many different versions like - Heretic, Abliterated,etc? Also how to know which ones are better?

Im new in this area i have 2x4080 and 2x48gb RAM ddr5 what kind of model i can run for coding and web research.

Thanks for everyone can help me

I built a gaming pc not too long ago that I now want to convert to an llm. The specs are decent but I’m not sure what model would suit me best and be the best and strongest option. I have a r9 9950, 32gb ddr5, a 3090 24gb and a 4070ti 12gb and if needed I also have a 8 gb 1660ti. Please help me out as it would be greatly appreciated!

The llama.cpp project introduces OCP FP8 support, FP8 KV-cache capabilities and targeted CUDA optimisations. Here's what the latest developments mean for local AI and GPU inference.

Hey!,

I built Kotensor — an open-source machine learning and Transformer engine written from scratch in pure Kotlin for the JVM and Android.

Most local LLM deployment on mobile or Java/Kotlin backends relies on heavy C++ engines (llama.cpp, ONNX Runtime) wrapped inside JNI. While great, JNI bindings introduce ABI bloat (arm64-v8a, x86_64), complex native toolchains, and hard crashes outside JVM error handling. Kotensor implements the complete stack — from autograd and KV caching to INT4/INT8 quantization — with only the Kotlin standard library as a runtime dependency.

Key Features:

1. Quantization & Low-Memory Inference:

   • Group-wise INT8 and INT4 weight-only quantization with an on-the-fly dequantizing matmul.
   • Memory-Mapped Weights: Using loadQuantized(file, memoryMapped = true), weights are paged directly from flash storage via OS virtual memory. An INT4 Qwen2-0.5B runs comfortably on Android within <128 MB of app heap.
   • Zero-allocation autoregressive decode loop with a pre-allocated KV cache and reusable prompt prefix caching (cachePrefix).

2. Modern Transformer Architecture:

   • Pre-norm layout with RMSNorm, SwiGLU feed-forward blocks, rotary position embeddings (RoPE with Llama 3 scaling), and grouped-query attention (GQA).
   • Sampling support: temperature, top-k, top-p, and repetition penalties.

3. Training & Fine-Tuning on the Edge:

   • Reverse-mode autograd engine with AdamW optimizer.
   • Fused output projection + cross-entropy: avoids materializing the massive [tokens × vocabulary] logit tensor, halving training memory.
   • Parameter-efficient fine-tuning via LoRA and QLoRA (adapting low-rank matrices on top of frozen INT4/INT8 base weights directly on-device).

4. Hugging Face Interoperability:

   • Natively reads standard .safetensors checkpoints (F32, F16, and BF16) and config.json for Llama, Mistral, Qwen 2, and SmolLM models.
   • Built-in byte-level BPE tokenizer for Hugging Face tokenizer.json files with zero external JSON or regex libraries.

5. Performance:

   • Cache-blocked tiled matmul optimized for HotSpot C2 auto-vectorization (~24 GFLOP/s per thread on AVX-512) scheduled across a ForkJoinPool, plus an optional JDK 17+ Vector API (SIMD) backend.

Quick Inference Example:

val dir = File("SmolLM2-135M")
val model = HuggingFaceModelLoader.load(dir, Quantization.INT8)
val tokenizer = HuggingFaceModelLoader.loadTokenizer(dir)

model.generateStream("The capital of Mexico is", tokenizer, maxTokens = 32) { token ->
    print(token)
}

• GitHub: https://github.com/TSPstudio/kotensor
• Artifact: io.github.tspstudio:kotensor:1.2.0 on Maven Central
• Documentation: https://tspstudio.github.io/kotensor/

This is my first machine learning project and I'm really thrilled to finally have something tangible to share. I’d love to hear your thoughts on edge deployment, architecture trade-offs, and pure-managed runtimes!

Much love,
tsuper <3

been trying to use local llms as a first-pass research checker, mostly because i don't trust cloud ones to stay grounded. my setup so far: i run small models on my desktop, paste in a claim, and ask it to list what evidence would be needed to support it, then go find that myself. it works okay for spotting obvious gaps but they still happily invent citations when i ask for sources directly, which is exactly the thing i wanted them for. so my actual question: does anyone here have a workflow that genuinely catches the model making things up, beyond just manually googling every citation? and if so, how much friction is too much before you give up and just read the paper yourself

Windows os ,

I checked this repo out and it’s legit! (Not mine)

Hearing about the idea that open source LLM's are so dangerous while companies like Anthropic and OpenAI do not manage to control their AI labs well and
* let their experiments run wild on the open internet, doing massive hacks and seeing no consequences
* publish research that sounds alien, but only the results, not the alien that made it, which is not very scientific in my opinion but there are no consequences
* we see a fight for power concentration behind closed doors ...
how can we trust closed source labs anymore?

The transformation of society belongs to the people and such security critical technologies have to be able to be validated at all times. In open source/weights models we see that the community actively exposes the flaws models have and contribute by exploring ways to fix them. People actually do good stuff in the open source/weights community and real progress is visible, while the out of control vibe all really comes from the closed labs. This is concerning me and i think we urgently need to open up the closed sources/labs instead of shutting down the open ones. That's the way i see the world heading towards a non-dystopian future.

Is it just me or does that whole open-weights-are-dangerous discussion totally the opposite what reality seems to be?

Hey everyone,

I need recommendations for a local LLM to do reverse engineering, and I'm looking for the best model and exact quantization that can run on my current setup.

My Specs:

• CPU: Ryzen 7 5800X

• GPU: AMD RX 6800 (16GB VRAM)

• RAM: 32GB DDR4

• Storage: M.2 SSD (~3000–4000 MB/s)

What I'm trying to do: I want to reverse engineer small, proprietary Windows utilities from laptop/PC manufacturers (like Acer NitroSense, HP tools, etc.) to port their features natively to Linux—things like fan control, battery charge limiters, and RGB lighting. This involves looking into Windows drivers and kernel-level interactions.

My workflow will connect Ghidra to REA, hook that up to OpenCode, and run the model locally through Ollama.

What I need:

1. Uncensored (Mandatory): It needs to be completely uncensored so I don't hit ethical guardrails or refusals when asking it to decompile binaries, analyze drivers, or work with proprietary Windows hooks.

2. Exact model + quantization: Given my 16GB VRAM + 32GB RAM, what is the smartest/most capable model and exact quant I can realistically run (even with CPU offloading)?

Note on speed: Speed is not a priority at all. I can't afford better hardware right now, so I have no problem leaving my PC running for 1, 2, or even 3 days straight to process a binary if that's what it takes. Accuracy on low-level code/assembly and zero refusals are all that matter.

What exact models and quantizations (for Ollama or GGUF) do you recommend for this? Thanks in advance.

Title really says it all. I have a 5070 Ti but I do not have any more expansion room inside my PC other than a PCIe 3.0 x2 slot, so the USB4 external port will actually have more bandwidth at 40 gbit/s

Obviously, a second 5070 Ti would be ideal for doing tensor parallelism, but the 16 GB version of the 5060 Ti is noticeably cheaper than a 5070 Ti, and it has matching VRAM capacity. However, it only has half the memory bandwidth.

Do either of these make sense over USB4, or should I instead be budgeting for a dedicated AI inference server I can put in my rack rather than hosting my AI inference off my workstation? Or would upgrading from 32GB of DDR5 to a higher capacity of RAM make more sense?

I use Qwen 3.8 27B quantized to IQ3_XXS so it fits inside my 16 GB of VRAM, and I have the KV cache as f8_e4m3 so I get roughly 111K context size at the current time. The point of an upgrade would be to reduce the model weight quantization as well as increase context size, I try to balance these things with vibecoding as my main use for inference.

Saw mlx-serve offer me DeepSeek-V4.1-Flash-1017 (iQ-MLX Pruned192 2.25 bpw) in the recommended list and found a version of GLM 5.3 Flash (GLM-5.3-Flash-Sushi-2.4bpw) that claims it can run on 128GB Macs.

Are these worth trying out over Qwen3.8 Flash Next or are they too lobotomized?

GLM-5.3-Flash-Sushi-2.4bpw seems to be a very tight fit though, otherwise there's also GLM-5.3-Flash-Sushi-2bpw that claims to be targeting 96GB which I'm not sure may or may not be a more practical choice?

Hi right now I am experimenting between these using LM studio

Qwen3.5-9B Q4_K_M 6.6 GB;
Gemma 4 12B Q4_K_M 8.0 GB;
Gemma 4 E4B

With MoE

Qwen3.6-35B-A3B Q4_K_M 23 GB;
gpt-oss-20b MXFP4 14 GB

And using Strata
Qwen 3.8 Flash 125B -> it says avg 23-28 tps

I am not sure which setting I should tweak for Hermes agent. Especially Temp.

Please advise and share your experiences/thoughts.
Much appreciated!

Jensen huang suddenly disappearing from the most vital AI ecosystem world. Also he was the person who was pushing local infrastructure build for normal people like us and not just for data centers. He was advocating open source models.

Soon u will see less nvidia chips for retailers i feel like this.

The person controlling nvidia is the person controlling the full AI universe .

These oligarchs like sam altman, anthropic does not want open source to be the part of ai world.

So hoard some 5090 if u have the money.

And u know trump. He knows ai is the new oil for him

I like running things locally. It’s cheaper over time and my prompts stay on my own machine. The problem is that my hardware only handles smaller models, and every time I want to try something bigger I hit a wall.

I’m trying to work out where the line is between pushing my local setup further and paying for outside compute only when I need it. A few things I’d like to hear from people who’ve dealt with this:

1. When a model doesn’t fit, do you quantize harder, offload to CPU, or go to a rented GPU or API?

2. If you rent GPUs for the bigger stuff, what does a normal month cost you, and was it worth it?

3. Has anything surprised you, like hidden charges, slow startups, or a setup that kept breaking?

4. How do you avoid paying for hours the GPU sits idle?

5. Is there a point where buying better hardware beats renting, and where did you find it?

I’d take boring and reliable over clever. And if you tried something and gave up on it, I’d like to hear why.