---
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
- 2-bit
- apple-silicon
- 256k-context
- thinking
pipeline_tag: text-generation
language:
- en
---

> [!TIP]
> **KV-cache quantization without any fork (recommended, 2026):** upstream
> llama.cpp/Ollama now cover this natively — use `-ctk q8_0 -ctv q8_0`
> (~half KV memory, negligible quality loss: perplexity +0.002–0.05) or
> `-ctk q4_0 -ctv q4_0` (~quarter memory, ≈7.6% perplexity increase). In
> Ollama: `OLLAMA_KV_CACHE_TYPE=q8_0` with `OLLAMA_FLASH_ATTENTION=1`. Keep
> K and V types symmetric to stay on the fast fused Flash-Attention path.
> Since April 2026, mainline llama.cpp also applies Hadamard rotation to
> KV activations ([PR #21038](https://github.com/ggml-org/llama.cpp/pull/21038)),
> which greatly improves low-bit KV quality (opt-out:
> `LLAMA_ATTN_ROT_DISABLE=1`).
>
> The RotorQuant/TurboQuant fork flow below is **experimental/legacy**: the
> TurboQuant llama.cpp PR was closed without merging (June 2026) and the fork
> is unmaintained relative to mainline. It is NOT required to use this model.
<!-- kv-upstream-note -->

# Mistral-Small-4-119B-TurboQuant-MLX-2bit

**Dual compression: 2-bit MLX weight quantization + TurboQuant KV cache quantization** for Mistral Small 4 on Apple Silicon.

This repository provides a 2-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. Aggressive compression for running on consumer Apple Silicon hardware.

## Overview

This model applies two complementary compression techniques:

1. **2-bit weight quantization (MLX)** -- reduces model weights from ~238 GB to ~30 GB
2. **TurboQuant KV cache quantization** -- reduces KV cache from ~32 GB to ~8 GB at 256K context

This enables running a 119B-parameter MoE model on Apple Silicon Macs with 64 GB+ unified memory.

## 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 | 2-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 (2-bit MLX + TurboQuant)** | **~30 GB** | **~8 GB** | **~38 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. The 2-bit quantization trades some quality for significantly reduced memory. Expect modest degradation on complex reasoning tasks compared to 4-bit.

## Quickstart

```python
from mlx_lm import load, generate

model, tokenizer = load("majentik/Mistral-Small-4-119B-TurboQuant-MLX-2bit")

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)
```

## About the RotorQuant / TurboQuant labels

RotorQuant and TurboQuant are this project's **release labels**, not distinct
quantization algorithms — for any given tier, both brand repos carry
byte-identical weights produced with the standard MLX / llama.cpp quantizers.
No brand-specific speedup is claimed or measured. The KV-cache fork these
labels originally referred to is legacy; for KV-cache memory savings use the
upstream options described above (`-ctk/-ctv q8_0`, `OLLAMA_KV_CACHE_TYPE`).

## 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-MLX-4bit](https://huggingface.co/majentik/Mistral-Small-4-119B-TurboQuant-MLX-4bit) -- 4-bit MLX variant
- [TurboQuant Paper (arXiv: 2504.19874)](https://arxiv.org/abs/2504.19874)