Instructions to use batiai/Mistral-Medium-3.5-128B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use batiai/Mistral-Medium-3.5-128B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="batiai/Mistral-Medium-3.5-128B-GGUF",
	filename="mistralai-Mistral-Medium-3.5-128B-IQ3_XXS.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use batiai/Mistral-Medium-3.5-128B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama-cli -hf batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama-cli -hf batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS

LM Studio
Jan

vLLM

How to use batiai/Mistral-Medium-3.5-128B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "batiai/Mistral-Medium-3.5-128B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "batiai/Mistral-Medium-3.5-128B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS

Ollama
How to use batiai/Mistral-Medium-3.5-128B-GGUF with Ollama:
```
ollama run hf.co/batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS
```

Unsloth Studio new

How to use batiai/Mistral-Medium-3.5-128B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for batiai/Mistral-Medium-3.5-128B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for batiai/Mistral-Medium-3.5-128B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for batiai/Mistral-Medium-3.5-128B-GGUF to start chatting

Pi new

How to use batiai/Mistral-Medium-3.5-128B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use batiai/Mistral-Medium-3.5-128B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS

Run Hermes

hermes

Docker Model Runner
How to use batiai/Mistral-Medium-3.5-128B-GGUF with Docker Model Runner:
```
docker model run hf.co/batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS
```

Lemonade

How to use batiai/Mistral-Medium-3.5-128B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/Mistral-Medium-3.5-128B-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.Mistral-Medium-3.5-128B-GGUF-IQ3_XXS

List all available models

lemonade list

Mistral Medium 3.5 128B GGUF — Quantized by BatiAI

Quantizations of Mistral Medium 3.5 128B (Dense, Modified MIT) — frontier coding model on a Mac. Built and verified by BatiAI for BatiFlow.

Why Mistral Medium 3.5?

SWE-Bench Verified: 77.6% — second only to Gemini 3.1 Pro Preview (78.8%)
Tau3-Telecom: 91.4% — top-tier agentic benchmark
128B Dense — Mistral's first flagship merged model (instruction + reasoning + coding in one weight set)
Modified MIT — open weights, commercial-friendly
256K context window + multimodal vision capabilities
Configurable reasoning effort per request
Released April 29, 2026

Quick Start

ollama pull batiai/mistral-medium-3.5:iq4

Available Quantizations

Quant	Size	VRAM target	Recommended For
IQ3_XXS	~50GB	~64GB	96GB+ Mac (compact)
IQ4_XS	~70GB	~84GB	128GB Mac (recommended)
Q5_K_M	~90GB	~108GB	192GB+ Mac (highest quality)

RAM Requirements

Your Mac RAM	IQ3 (~50GB)	IQ4 (~70GB)	Q5 (~90GB)
64GB	⚠️ Heavy swap	❌	❌
96GB	✅ Fast	⚠️ Tight	❌
128GB	✅	✅ Recommended	⚠️ Tight
192GB+	✅	✅	✅ Best
512GB	✅	✅	✅ Headroom

Coding Powerhouse on a Mac

Benchmark	Mistral Medium 3.5
SWE-Bench Verified	77.6%
Tau3-Telecom (agentic)	91.4%
Context window	256K

A coding model that goes head-to-head with cloud frontier (Gemini 3.1 Pro), runnable entirely on-device on a 128GB MacBook Pro.

Model Comparison — Frontier Class on Mac

Your Mac	Best Pick	Speed	Use Case
32GB	`batiai/nemotron3-nano:iq4`	—	General agentic
96GB	`batiai/mistral-medium-3.5:iq3` (this)	—	Coding agents
128GB	`batiai/mistral-medium-3.5:iq4` (this)	—	Frontier coding
128GB	`batiai/minimax-m2.7:iq3`	36.7 t/s	229B Dense, GDPval
192GB+	`batiai/mistral-medium-3.5:q5`	—	Max quality
512GB	`batiai/kimi-k2.6:iq4`	—	1T MoE, SWE-Bench Pro 58.6

Why BatiAI Quantization?

	BatiAI	Third-party
Source	Official Mistral weights	Re-quantized
imatrix	✅ wikitext-2 200 chunks	Standard
Vision	(text-only quants)	(text-only quants)
BatiAI signed	✅	—

Technical Details

Original Model: mistralai/Mistral-Medium-3.5-128B
Architecture: Dense decoder-only, 128B params
License: Modified MIT (commercial-friendly)
Quantized with: llama.cpp + imatrix
Quantized by: BatiAI

About BatiFlow

BatiFlow — free, on-device AI automation for Mac. 5MB app, 100% local, unlimited. 60+ tools — KakaoTalk, iMessage, Slack, Calendar, browser, file system.

License

Quantized from mistralai/Mistral-Medium-3.5-128B. License: Modified MIT.

Benchmarks

Machine	Quant	Cold start	Prompt eval	Token gen	Tested
MacBook Pro M4 Max 128GB	IQ3_XXS	35.316s	36.7 t/s	6.82 t/s	2026-05-04

Downloads last month: 697

GGUF

Model size

125B params

Architecture

mistral3

Hardware compatibility

3-bit

4-bit

5-bit

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