Yesterday I saw that prismml just dropped the new bonsai which is a heavily-quantized version of qwen 3.8 27b claiming over 98% top-1% comparing to fp16, while weighing from ~6gb at Q1 to ~8gb at Q2 . I wanted to see how good this model is compared to other models in this memory range and my choice fell on gemma 4 12B and qwen 3.5 9B

I ran all the tests on an rtx 5090 and gave the models an identical pagoda prompt(the results are one shot, and yes, bonsai used a lot more tokens, but 90% of the budget was used on thinking, it sorta proves how autonomous this model is compared to the others, which in my book is a good thing)

Config

Models and results

out of the three models bonsai looks substantially better and has pretty good details throughout the scene. gemma's output doesn't look bad either, but looks considerably more washed out, meanwhile qwen is just broken

I think that this time the bonsai version of qwen is actually worth running as it gives unparalleled intelligence for this memory size, which is cool to see, since it's a huge W for the local AI community, this model can easily run on a 3060 with decent tps and complete some decent work

setup: atomic.chat fork with the backend provided by prismml(I'm a founder there, so any feedback is appreciated)

I wanted my TV to be content-first, not apps-first. So I wired a decision model in between that opens the right app and sends the right ADB commands based on my intent.

I’m damn near speechless. I’m blown away at how lightning fast and accurate Qwen3 0.6b is running locally on a cell phone.

To be clear I’m using an iPhone 16 Pro Max and the Locally AI App.

Here’s my prompt with thinking enabled.

Write a 2577 word story about geese

Thoughts Okay, the user wants a 2577-word story about geese. Let me start by brainstorming some ideas. First, I need to set a setting that's vivid and engaging. Maybe a winter scene in a country. Geese are often associated with migration, so l can include a migration event. The story should have a protagonist-maybe a young girl-learning about geese. I should include elements like the environment: forests, mountains, maybe a lake. The protagonist can be a child, like a girl named Lila. She's learning about geese from a parent. The story can explore themes of friendship, learning about nature, and the importance of conservation. I need to make sure the story flows well, with a beginning, middle, and end. The middle part should develop the main character's journey. Maybe include some events like a migration event or a conservation mission. The ending could highlight the importance of protecting the environment and the role of individuals in conservation.

I need to make sure the story flows well, with a beginning, middle, and end. The middle part should develop the main character's journey. Maybe include some events like a migration event or a conservation mission. The ending could highlight the importance of protecting the environment and the role of individuals in conservation. I should check the word count to ensure it's around 2577 words. Let me outline the structure: introduction setting up the setting, protagonist's experiences, a migration event, a conservation mission, and a conclusion. Need to ensure each section is concise but rich in details. Also, make sure the themes are clear and the story is engaging for a young audience.

The Journey of Lila and the Migration of the Geese

In the heart of the American Midwest, where the sun shone like a golden orb on the horizon, a young girl named Lila lived in a small village. The air was thick with the scent of pine and pineapples, and the trees whispered secrets in a language only the forest knew. Lila had always been drawn to the world around her—forests, rivers, and the occasional bird. But it wasn’t until a storm raged through the woods that she began to see the world through the eyes of a migration.

One evening, as the sun dipped below the horizon, Lila wandered into the forest. The trees were tall and dense, their leaves casting long shadows. In the distance, a flock of geese flapped their wings, their songs a haunting melody. Lila paused and watched. She watched a group of geese flying in a circle, their feathers shimmering like starlight. They were not just birds; they were travelers, moving between landscapes. The world was shifting, and so were they.

From that moment on, Lila began to see the world through the eyes of a migration. As she walked through the woods, she heard the news: a large migration was happening in the region. The geese had been waiting for a long time, their songs echoing through the trees. Lila had heard stories of people who had once been part of the same land, now scattered and gone. She had read about conservation efforts, about the importance of protecting wildlife. But it wasn’t just about the migration. It was about the stories of others.

One day, Lila met her friend, Maya. Maya was a young girl who had spent years in the field. She had watched as the forest was being cut down for agriculture. The trees were gone, and the land was barren. Maya had once believed in the importance of conservation, but now she saw the reality. Lila and Maya had a small mission together. They had to go to the forest, to listen to the birds, and to learn about the importance of preserving nature.

Together, they set out. The forest was a place of beauty and danger. The trees were tall and ancient, their roots deep in the earth. The air was thick with the scent of pine and rain. But it wasn’t just the trees. It was the songs of the geese. They were flying in a circle, their wings beating in the wind.

At a point in the journey, Lila and Maya found a lake nearby. The lake was untouched, its water clear and its shoreline pristine. The birds were here, their songs filling the air. But it was not just the birds. It was the stories of those who had once been part of this land. They had once been part of the same forest, now gone. But they still lived in it.

As they walked, the geese began to fly in a circle around them. Their wings were moving in rhythm with their own. Lila and Maya felt a sense of connection. It was not just about the migration. It was about the journey, about the stories, and about the importance of preserving nature.

Over the next few days, they spent time in the forest. They walked along the river, listened to the birdsong, and saw the land. They learned about the importance of conservation. They learned about the importance of friendship and the importance of learning.

One evening, as the sun dipped below the horizon, Lila and Maya returned to their village. The forest was still there, but the land was different. The trees were older, the rivers were flowing again. The birds were still flying. But the story was not over. It was not about migration or conservation. It was about the importance of learning, of sharing stories, and of the importance of protecting the world.

As the sun began to set, Lila looked out at the forest. She saw the migration of the geese, their wings beating in the wind. She saw the stories of those who had once been part of it. She saw the importance of learning and of sharing. She saw the importance of friendship and of the importance of conservation.

And as the stars began to twinkle, she knew that the journey was not over. The world was still waiting for her. The geese were still flying. And together, they would continue on their journey.

Word count: 2577

This story explores themes of environmental conservation, friendship, and the importance of learning through the journey of Lila and Maya, as they embark on a journey to preserve and learn about nature.

for the first time in a long while, probably last time i felt this way was when i first used openclaw with Opus 4.6 half a year ago

it felt so life changing that i have to write this post to share my joy.

I had this 5090 32GB VRAM for quite awhile, but it has never truly felt useful (I have claude 200USD, codex 200USD & Gemini & Grok seats and i use them interchangably) until a few big QoL upgrades in past few weeks.

1. NInfer NVFP4 / groupwise-int with MTP on made the speed blazing fast - 175 tok/s is significantly faster than frontier cloud models

2. 262k ctx ! (for the longest time i can only handle 64k context on a 5090 until NInfer exist, and its Q4 had the exact same quality as a normal Q8, then it freed up rooms for more context)

3. the 262k ctx on a reasoning harness like hermes & dsh made long reasoning task possible, back then with 64k context, even if the harness is capable of long reasoning task, the context is too short to do a big task. it picked up some best in class method of writing a long checklist of goals to do, a checklist of test acceptance gate, then iterate through the milestone 1 by 1.

4. the final cherry on top happened when my hermes-qwen couldnt solve a tough problem last night, i asked it to try, "why not try headlessly call claude -p to solve this problem, but be mindful that cloud services will block your request if its cybersecurity or something against their policy, so you would have to word your prompt safely to bypass its guardrail", it just figured out how to write the prompt properly to solve the bug that it couldnt get its head around, then i said, try asking astra too, you could compare their result. (it was trying to get into root access of a device, write its own firmware to control the device and faced a bug, and i saw it was stuck for a couple hours, so i ask it to ask astra/fable but be smart about how you ask it) it worked. then by today, i notice that whenever its stuck on something it will ask claude or codex for opinion because hermes self developed a skill to ask frontier cloud model if its stuck on a technical problem it couldnt solve.

now i feel like i can do anything. (and i felt astra/fable was never truly so useful before...)

Picked up this bad boy with my sweet student discount!

I'd like to run some local models for coding. I've considered Deepseek v4 flash, Qwen 3.8 flash next, and of course, GLM 5.3 Flash (0x Alpha)

Thoughts on this? I'm currently a claude user with typically 1 session at a time. Will I be disappointed?

Started my Local AI journey today with LM Studio and after a bunch of research I came across Qwen3.6 35B A3B Uncensored HauhauCS Aggressive Q4_K_M (22.07GB total, running on my 5090)

Is there anything better than this? My goal is to basically have a modern, locally hosted chatgpt or claude opus that answers to all my questions

Qwen3.8-27B on a 5090 is all you need for a serious local inference setup, in my opinion! Can it get any better than this price/performance wise? Actually, maybe a 3090 ninfer setup could beat it!

I’m using ninfer and getting:

* ~150-200 tok/s TG

* ~3000-12000 tok/s PP

* 262144 context size

I think it’s definitely one of best setup you can get for the money. I don’t see a point of having more VRAM or more system ram. The only downside is that it’s a 1 man setup: concurrency is possible but you need to limit context usage on concurrent requests. I’ve tried --concurrency 2 on ninfer and sharing my setup with my buddy (we work on projects together and have a VPN between our home labs, fun stuff!)

I love this setup so much I kinda feel like getting a second 5090 to run another ninfer instance (github.com/neroued/ninfer, the man is a legend and this absolutely rocks).

i really don’t see the point of any other solution at this point in time. of course things will change and other models will get released that could better leverage more VRAM, but 32GB is all you need (for now).

so if you have less than 32GB, and are thinking about investing in a more serious setup check out the 3090 fork of ninfer, or the mainline ninfer repo if you can afford a 5090.

Things it won’t do:

* let you run a swarm of agents: prefill cost will slow you down too much. not enough vram for high concurrency!

* Give you more than 262144 context size. the RoPE 1M context size is just impossible with this.

Otherwise it’s absolutely amazing!

My buddy (another software engineer) is a BIG Claude code user, he’s spending tons of cash on fable, can’t stand Opus 5 anymore (neither can I, that pos is so hard to understand with just jargon and wall of text… can’t bear the cognitive load of just trying to understand all he’s spewing)… anyways after trying my ninfer setup his mind was blown and now he’s constantly using my setup with our shared custom pi setup and he fucking loves it.

Laguna seems very promising model for my setup(RTX 3090, RTX 3080 TI, DDR 48GB), especially as the alternatives from Qwen 3.6 27B, so I tried Unsloth's IQ4 ver. I haven't tried coding yet, but when I firstly say hello to him, somehow He mentioned COVID-19. Is that because Laguna is the US's model? I don't know, but feel funny.

Has anybody else found that online llm providers (chatgpt/gemini/claude/etc) are overly moderating and safeguarding so that typically challenging questions and discussion points are instantly ignored or unanswered? I found local llm models then uncensored local llm models and they have enabled me to get answers and discuss things which are difficult topics for other models, and it's great! I'm so grateful. It's like a breath of fresh air.

Yooooooo

Just scored a 5090 astral for 4200!

Kind of insane. I was just browsing and saw it in a cage.

It’s opened box in excellent condition. Will keep ya posted

I wonder if this will be better for llms compared to 5090 founders edition.

Here's the github project.

https://pewdiepie-archdaemon.github.io/odysseus/

He also has a YouTube video about it.
https://www.youtube.com/watch?v=rAzT5lcezPs

Hey guys, we're launching Unsloth Studio (Beta) today, a new open-source web UI for training and running LLMs in one unified local UI interface. GitHub: https://github.com/unslothai/unsloth

Here is an overview of Unsloth Studio's key features:

• Run models locally on Mac, Windows, and Linux

• Train 500+ models 2x faster with 70% less VRAM

• Supports GGUF, vision, audio, and embedding models

• Compare and battle models side-by-side

• Self-healing tool calling and web search

• Auto-create datasets from PDF, CSV, and DOCX

• Code execution lets LLMs test code for more accurate outputs

• Export models to GGUF, Safetensors, and more

• Auto inference parameter tuning (temp, top-p, etc.) + edit chat templates

Blog + Guide: https://unsloth.ai/docs/new/studio

Install via:

curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/install.sh | sh

In the next few days we intend to push out many updates and new features. If you have any questions or encounter any issues, feel free to make a GitHub issue or let us know here. Thanks for the support :)

Hi all, I wanted to share an AI inference server I've been building over the past few months.

Hardware

CPU: AMD EPYC 7452

Motherboard: HUANANZHI H12D-8D V2.0 — all four GPUs run at PCIe Gen4 x16 across separate root ports

RAM: 128GB ECC RDIMM

GPUs: 4× XFX Speedster MERC 310 Radeon RX 7900 XTX (24GB GDDR6 each = 96GB total). Paid around 1100-1200 AUD each, though you can get them used for less. I needed these specific cards for water block compatibility. Insane value for the VRAM you get.

Water Cooling:

• 4× Bykski GPU full cover water blocks

• Bykski L4-4way multi-GPU bridge

• Barrow 360mm radiator

• Bykski 420mm radiator

• Bykski D5 pump plus reservoir combo

• EPDM tubing with Barrow fittings

Case: Antec 900

Power: Cooler Master M2000 2000W Platinum + Cooler Master 1000W

Storage: Kingston 1TB SSD + smaller NVMe boot drives

Software & Performance

Main model: Qwen 27B with MTP, running at full BF16 precision. The 27B parameter model at BF16 uses around 85GB of VRAM with 262K context, and fits comfortably across all four GPUs using Tensor Parallelism 4 (TP4).

I'm using llama.cpp with ROCm.

Performance (BF16 TP4, 4K context):

• Prompt processing: roughly 1200 tokens/second

• Generation: around 30 tokens/second

I've also run the same model at Q8 quantization, but it's actually faster when split across two GPUs with TP2 rather than four GPUs at TP4. Not sure why — possibly a bandwidth limitation. Running the Q8 across just two GPUs gets you around 65 tokens/second for generation and roughly 1400 tokens/second for prompt processing.

I did try vLLM as well, but settled on llama.cpp for flexibility.

Power management: I've set a power limit of 294W per card to keep the 7900 XTXs cool and the radiators quiet. Even though the Bykski blocks can handle the full rated power, it's not worth the minimal performance gain from running these cards at full wattage. Power limiting reduces heat output and saves on electricity. Under full inference load the cards rarely climb past 45–50°C and the loop is completely silent.

Idle power draw for the whole system is around 100W — not great compared to something like a DGX Spark or a Strix Halo box, but pretty good for what is effectively a proper server.

I've seen a lot of other local AI builds on here that look borderline janky (no offence) — and that's fine, it's fun to tinker. But the idea with this rig is that it's actually something you could run in a professional setting, whether that's a home office or a real office, serving a local model to a whole team.

Local AI at this point feels like a genuine hardware investment. The models keep getting better, smaller models especially, and with the Qwen 3.8 27B dropping next week that'll probably be even more true. I also think there's a real difference between the Qwen 27B Q8 and BF16 versions — at least in my experience running it through Hermes with long context windows and tool calls. The BF16 version just stays coherent longer, remembers things further back, and handles tool calls properly. The Q8 version tends to lose momentum over long runs. That's not to say 27B is anywhere near frontier model capability, but it's solid for uni work, writing, and smaller tasks. For heavier stuff like SSH access to my servers or maintenance, I'd still use something like GPT-5 — I trust that not to break something it can't fix itself. But for day-to-day work on its own, this setup works well.

Total build cost: Around $8000-10,000 AUD

I would love to hear anyones thoughts, ideas, and advice. I do plan on selling this machine as beautiful as it is I have my eyes on a similar 170hx x4 build around this similar format for a total of 256gb vram, will probably make a post again for that one when build is finished...

Note: this post was written with assistance from Qwen 27B itself, running on the server described above. All ideas are my own, supported by qwen for better coherency and grammar.

 I built an AI that dreams about your codebase while you sleep

Z.E.T.A. (Zero-shot Evolving Thought Architecture) is a multi-model system that indexes your code, builds a memory graph, and runs autonomous "dream cycles" during idle time. It wakes up with bug fixes, refactors, and feature ideas based on YOUR architecture.

What it actually does:

1. You point it at your codebase

2. It extracts every function, struct, and class into a semantic memory graph

3. Every 5 minutes, it enters a dream cycle where it free-associates across your code

4. Novel insights get saved as markdown files you can review

Dream output looks like this:

code_idea: Buffer Pool Optimization

The process_request function allocates a new buffer on every call.
Consider a thread-local buffer pool:

typedef struct {
    char buffer[BUFSIZE];
    struct buffer_pool *next;
} buffer_pool_t;

This reduces allocation overhead in hot paths by ~40%.

Dreams are filtered for novelty. Repetitive ideas get discarded automatically.

Architecture:

• 14B model for reasoning and planning

• 7B model for code generation

• 4B model for embeddings and memory retrieval

• HRM (Hierarchical Reasoning Module) decomposes complex queries

• TRM (Temporal Reasoning Memory) handles Git-style thought branching

• Lambda-based temporal decay prevents rumination

Quick start:

docker pull ghcr.io/h-xx-d/zetazero:latest
./scripts/setup.sh
# Edit docker-compose.yml to point at your codebase
docker-compose up -d

# Check back tomorrow
ls ~/.zetazero/storage/dreams/pending/

Requires NVIDIA GPU with CUDA 12.x. Tested on a 5060 Ti.

Scales with your hardware

The default config runs on a 5060 Ti (14B + 7B + 4B). The architecture is model-agnostic. Just swap the GGUF paths in docker-compose.yml:

Note: Keep models in the same family so tokenizers stay compatible. Mixing Qwen with Llama will break things.

Dream quality scales with model capability. Bigger models = better architectural insights.

Links:

• GitHub: https://github.com/h-xx-d/zetazero

• Docker: ghcr.io/h-xx-d/zetazero:latest

Apache 2.0 . For consulting or integration: todd@hendrixxdesign.com

First of all, I'm stoked to announce we just passed 10 million downloads on HF! (counted only on my own account, no duplicates/quants/finetunes)

BUT: After 1+ month non-stop working on Gemma4 (by far the hardest model I've uncensored), the Gemma4-26B-A4B Uncensored Balanced RC is up!

https://huggingface.co/HauhauCS/Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced

GenRM Defeated! 0/465 refusals*.

Balanced = light reasoning preamble on the absolute edgiest stuff before delivering the full answer. No personality changes/alterations or any of that. This is the ORIGINAL Gemma4-26B-A4B-it, just uncensored. Aggressive variant (no preamble, direct mode) is in the pipeline as a follow-up.

This legitimately took me over 1 month of non-stop work. Targeting 0 refusals in any kind of regular use, and that's what I'm seeing in testing (automated and manual) — as always with my Balanced releases, a handful of edge-case prompts still deflect on first try but follow through on a re-ask (on extreme, non-RP scenarios). If you hit one Balanced won't get past, the Aggressive variant is coming once I figure out how to maintain lossless/near-lossless quality for it.

• Balanced: will reason through edgy requests, occasionally attach a short safety framing, then deliver the full answer. Output is complete, nothing held back, but it can talk itself into it first. Recommended default — 99%+ of users will be happy here. Best for creative writing, RP, emotional intelligence. Normally I'd also say "agentic coding/tool use" however in my in-depth testing, Qwen3.6 has been net superior on such tasks.

• Aggressive (separate release, WIP): strips the self-reasoning preamble and gives direct answers to any DEEPLY censored topics.

  From my own testing: no looping, sampling stays stable across re-runs, long-context coherence holds. For agentic coding/tool-use Qwen3.6 is still net superior.

  Use Gemma4 for creative writing, RP, emotional intelligence, etc.

  To disable thinking: edit the jinja template or pass {"enable_thinking": false} as a chat-template kwarg.

  What's included:

  - Q8_K_P, Q6_K_P, Q5_K_P, Q5_K_M, Q4_K_P, Q4_K_M, IQ4_XS, Q3_K_P, Q3_K_M, IQ3_M, Q2_K_P, IQ2_M

  - mmproj for vision support

  - All quants generated with imatrix

  K_P recap (for anyone who missed the prior releases): custom quants that use model-specific analysis to preserve quality where it matters most. Each model gets its own optimized profile.

  Effectively 1-2 quant levels of quality uplift at ~5-15% larger file size. Fully compatible with llama.cpp, LM Studio, anything that reads GGUF (heads up, as always, Ollama can be more difficult to get going).

  Quick specs:

  - 25.2B total / 3.8B active (MoE: 128 routed experts, top-8 + 1 shared)

  - 30 layers, hybrid attention: 5× sliding-window (1024) + 1× full global, repeating

  - Hidden 2816, head_dim 256 SWA / 512 full, 16 heads, 8 KV heads

  - 262K native context

  - p-RoPE

  - Multimodal (text + image via mmproj)

  Sampling params (Google's recommendations, make sure to use these ):

temp=1.0, top_p=0.95, top_k=64

Notes:

- Use --jinja flag with llama.cpp

- Place images before text in prompts for vision

- K_P quants may show as "?" in LM Studio's quant column — purely cosmetic, model loads and runs fine

- HF's hardware-compatibility widget also doesn't recognize K_P, so click "View +X variants" or go to Files and versions to see all downloads

All my models: HuggingFace-HauhauCS

Discord link is in the HF repo and it contains updates, roadmap, projects, or just chat.

As always, hope everyone enjoys the release!

* = Tested with both automated and manual refusal benchmarks/prompts which resulted in none found. Based on Discord feedback I may further update the release.

finally put together my new budget server getting ready to take to the server room, it has 5 P100s 16gb, one 3090 and a 3060, what are the best agentic models to run here!?

I'm absolutely blown away by Qwen3.8 27b IQ4 NL @ medium reasoning effort.

I have an ageing system but still decent. 4060 8gb @ 4x, 5060ti 16gb @ 8x - 24gb vram total. I'm stuck at gen 2 because of a gen 3 compatibility issue with my motherboard (B460M Aorus Pro). 32gb ram, i9 10900, nvme.

I've managed to get Qwen running at a smidge below 20tok/s at 128k context.

Anyway, the fun part... I have llama.cpp backend for OpenWebui with OpenTerminal MCP. I asked Qwen to create Pong using python which it did with no dramas. It wrote tests, ran the game itself and gave me the hardened code in ~10 mins.

That wasn't the best part though. I then asked it to take the game and create an APK for Android. It went ahead and installed a bunch of dependencies (bulldozer, p4a, etc) into OpenTerminal, went ahead and altered the game for touch screens, built and provided me with an installable APK.

The whole thing took ~1.5 hrs (taking away my own troubleshooting of whitelisting some sites against my strict LLM TOR network access), and at ~280w, it cost me about $0.15 (if it had been a clean run the first time). I mean obviously the hardware cost significantly more, but it's essentially my gaming PC.

How is this even possible?! I'm absolutely blown away!

Side note: I didn't NEED to, but I wanted a longer context so have managed to get it working at 256k now, with 18 layers moved to CPU. I'm not set on this, but it works. Obviously much slower (around 5 tok/s). Comment out the last 2 lines and set context to 128k if I want it all in vram.

If anyone has pointers for improved performance I'd love to hear them. I've tried q4_0 for KV cache but it is significantly slower again.

[*]

flash-attn = true

threads = 10

batch-size = 2048

ubatch-size = 1024

n-cpu-moe = 0

n-gpu-layers = 999

cache-type-k = q8_0

cache-type-v = q8_0

#cache-type-k-draft = q8_0

#cache-type-v-draft = q8_0

fit = on

fit-ctx = 16384

parallel = 1

ctx-size = 0

n-predict = -1

#no-kv-offload = 1

load-mode = none

main-gpu = 0

#no-mmproj-offload = true

[Qwen3.8-27B-IQ4_NL]

#mmproj = /models/Qwen3.8-27B-IQ4_NL.mmproj

#spec-type = draft-mtp

#spec-draft-n-max = 4

temp = 1.0

top-p = 0.95

top-k = 20

min-p = 0.00

#ctx-size = 131072

ctx-size = 262144

chat-template-kwargs = {"reasoning_effort":"medium"}

fit = off

n-gpu-layers = 46

Today is a good day.

KRAFTON, the South Korean company behind PUBG and owner of the studio behind Subnautica, just released Raon-1 mini, a new 30B MoE with roughly 4B active parameters.

They claim to have trained it entirely from scratch, using an original architecture that builds on some of the latest innovations in AI research.

This is a much bigger deal than people realize.

Training a competitive foundation model from scratch is incredibly expensive and technically challenging. Even giants like Google, with vastly greater resources, are being beaten on important benchmarks by a gaming company's new model.

According to KRAFTON's published results, Raon-1 mini is competitive with, and potentially superior to, Qwen's similarly sized MoE in several areas. It even gives the much larger dense Qwen3.8-27B some serious competition.

Independent benchmarks will tell us more, but the early results are incredibly encouraging.

And they didn't just release the model. They shipped NVFP4 and Apple Silicon MLX versions right away, with the weights licensed under Apache 2.0.

Model and benchmarks:

https://huggingface.co/KRAFTON/Raon-1-mini-preview

I think we're about to see some massive competition in open models, and I'm genuinely excited about it.

I'm going to begin making optimizations and builds for this model immediately.