---
base_model: mistralai/Mistral-Small-4-119B-2603
library_name: mlx
license: apache-2.0
tags:
- turboquant
- kv-cache-quantization
- mistral
- moe
- sparse-moe
- multimodal
- quantized
- mlx
- 8bit
- apple-silicon
- 256k-context
- thinking
pipeline_tag: text-generation
language:
- en
---

# Mistral-Small-4-119B-TurboQuant-MLX-8bit

**Dual compression: 8-bit MLX weight quantization + TurboQuant KV cache quantization** for Mistral Small 4 on Apple Silicon.

This repository provides an 8-bit weight-quantized MLX conversion of [mistralai/Mistral-Small-4-119B-2603](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) with TurboQuant KV cache quantization support. Designed for efficient inference on Apple Silicon Macs.

Approximate model size: **~120 GB**

## Overview

This model applies two complementary compression techniques:

1. **8-bit weight quantization (MLX)** -- reduces model weights from ~238 GB to ~120 GB
2. **TurboQuant KV cache quantization** -- reduces KV cache from ~32 GB to ~8 GB at 256K context

Together, these make it feasible to run a 119B-parameter MoE model on high-memory Apple Silicon machines.

## Model Specs

| Property | Value |
|---|---|
| Base Model | Mistral Small 4 (March 2026) |
| Total Parameters | 119B |
| Active Parameters | 6.5B per token (Sparse MoE) |
| Architecture | Sparse MoE -- 128 experts, 4 active per token |
| Context Length | 256K tokens |
| Modality | Text + Images (multimodal) |
| Capabilities | Thinking / reasoning, tool use, multilingual |
| License | Apache 2.0 |
| Weight Quantization | 8-bit (MLX) |
| KV Cache Quantization | TurboQuant 4-bit |

## Memory Estimates

| Configuration | Weights | KV Cache (256K) | Total |
|---|---|---|---|
| FP16 baseline | ~238 GB | ~32 GB | ~270 GB |
| **This model (8-bit MLX + TurboQuant)** | **~120 GB** | **~8 GB** | **~128 GB** |

> **Note:** This is a Sparse MoE model -- only 6.5B parameters are active per token, so inference is fast despite the 119B total parameter count.

## Quickstart

```python
from mlx_lm import load, generate

model, tokenizer = load("majentik/Mistral-Small-4-119B-TurboQuant-MLX-8bit")

prompt = "Explain sparse mixture-of-experts architectures."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

response = generate(model, tokenizer, prompt=text, max_tokens=512)
print(response)
```

## What is TurboQuant?

TurboQuant ([arXiv: 2504.19874](https://arxiv.org/abs/2504.19874)) is a KV cache quantization method that compresses the key-value cache used during autoregressive generation. It supports 4-bit (default) and 2-bit (aggressive) modes. Because it targets the KV cache rather than weights, it stacks with weight quantization for compounding memory savings.

## KV-Cache Quantization Comparison

| Method | Prefill Speed | Decode Speed | Memory Savings | Reference |
|---|---|---|---|---|
| **TurboQuant** | Baseline | Baseline | High | [arXiv: 2504.19874](https://arxiv.org/abs/2504.19874) |
| **RotorQuant** | 5.3x faster | 28% faster | High | [GitHub](https://github.com/scrya-com/rotorquant) |

## Hardware Requirements

This model requires approximately 128 GB total memory at 256K context. Recommended hardware:
- Apple M2 Ultra (192 GB+)
- Apple M3/M4 Ultra (192 GB+)
- Mac Pro

## See Also

- [mistralai/Mistral-Small-4-119B-2603](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603) -- Base model
- [majentik/Mistral-Small-4-119B-TurboQuant](https://huggingface.co/majentik/Mistral-Small-4-119B-TurboQuant) -- KV cache only (no weight quantization)
- [majentik/Mistral-Small-4-119B-TurboQuant-MLX-4bit](https://huggingface.co/majentik/Mistral-Small-4-119B-TurboQuant-MLX-4bit) -- 4-bit MLX variant
- [majentik/Mistral-Small-4-119B-TurboQuant-MLX-2bit](https://huggingface.co/majentik/Mistral-Small-4-119B-TurboQuant-MLX-2bit) -- 2-bit MLX variant
- [majentik/Mistral-Small-4-119B-RotorQuant-MLX-8bit](https://huggingface.co/majentik/Mistral-Small-4-119B-RotorQuant-MLX-8bit) -- RotorQuant MLX 8-bit variant
- [TurboQuant Paper (arXiv: 2504.19874)](https://arxiv.org/abs/2504.19874)
- [MLX Framework](https://github.com/ml-explore/mlx)