The TurboQuant BreakThrough. Running Qwen3.5-35B-A3B-UD-IQ3_XXS.gguf with a Hermes Agent. Get $80K Enterprise Server Performance on a $800 House GPU.
The TurboQuant BreakThrough. Running Qwen3.5-35B-A3B-UD-IQ3_XXS.gguf with a Hermes Agent. Get $80K Enterprise Server Performance on a House GPU.
How One Single Mathematical Paper Increased LLM Performance by 600%.
- This is big, not just a little big but really, really REALLY flipping big. What used to require an Enterprise Grade Server with 2xH100 GPU compute and $80,000 can now be run on a House GPU for under $1000. Seriously.
- The implications are absolutely astounding. It enabled a home user to get access to 9 million tokens of compute generation daily - cost $0. People paying for Claude don't need it - at all.
- This has literally just came out a few days prior to this article, and as this is being written, usages for this new TurboQuant algorithm are still filtering backwards through the LLM model space.
- It brought serious level compute to the total masses. No longer do you need a DGI Spark, or a 4x3090 home cluster with a $1500 TR40 Server. Just one good decent 16 GB or less GPU card. What was the domain and moat of large enterprises is now available to anyone with gaming compute.
- You will need a custom fork the 'Turboquant' Fork of llama-cpp. We have written a full guide, follow it, try it then come back here.
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Have it installed and working per the guide? Now it gets interesting. The model in the above guide accelerated the kv-cache, but models were already coming out that accelerted the entire model! Consider:
Download this model to your present working directory with:
hf download Manojb/Qwen3.5-35B-A3B-UD-IQ3_XXS.gguf --local-dir .This thing is powerful, we were able to get the following on a 4080 w/ 16GB of VRAM.
262K Contexts, 105 Tokens/s, and 35B MoE (Mixture of Experts!)
So you can run it with powerful settings.
- If it runs out of RAM, just lower the -c 262144 to a lower number like 65536 etc.
- An example run setup:
/usr/bin/llama-server -m /home/c/PythonProject/TurboResearcher/models/Qwen3.5-35B-A3B-UD-IQ3_XXS.gguf --host 192.168.1.3 --n-gpu-layers -1 --flash-attn on --cache-type-k q8_0 --cache-type-v turbo3 -c 262144 --temp 0.7
By itself this is a very powerful LLM, SOTA level. Game changing level, now lets add Hermes that will connect it to the internet and activate its tooling:
- Tooling lets it look stuff up.
- Tooling activates your agentic workflows
- Tooling lets your LLM test it's own software to make sure it works.
- Tooling lets your LLM browse on the internet for you..
- Tooling lets you connect to it via Telegram, Discord, or other service and control it from a phone - anywhere in the world.
Setting up Hermes
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bashYou simply point it to your Lllama-cpp, which is usually on port 8080, by setting a custom LLM.
192.168.1.3:8080- Quick Setup:
- More Providers:
- Custom Endpoint
- Type in the IP:8080 where you are running Llama-cpp: In our case we had already added it.
- From here you can add connectors to control your system from anywhere via discord, telegram and various other chat interfaces..
Once it is installed it will show up as:
- Enabling chat and a zillion features.
Type /new you will start a new session. For instance:
What is the weather in New York.- Remember LLM's operate in isolation, they are a nucleus of mathematical weights, however tooling allows it and shows it that it can connect and do things.
- What is amazing here is the LLM is trying things, using the tool and looking up various web sites until it is able to confidently get an answer:
- It find the answer!
Why is this BIG BIG BIG?
- The cost is $0 + 1 House GPU. You are getting the capabilities near that of a World Class SOTA LLM for pennies!
Next up we do a much deeper dive into our tool exporation capabilities!
