Gemma 4 E2B It RotorQuant MLX 4bit

[!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), 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.

Gemma 4 E2B-it - RotorQuant MLX 4-bit

4-bit weight-quantized MLX version of google/gemma-4-E2B-it with RotorQuant KV-cache quantization. Optimized for Apple Silicon inference via the MLX framework. RotorQuant delivers 5.3x faster prefill and 28% faster decode compared to TurboQuant. A good balance between model quality and memory efficiency.

Approximate model size: ~1.2 GB

Model Specifications

Quickstart

import mlx.core as mx
from mlx_lm import load, generate

model, tokenizer = load("majentik/gemma-4-E2B-it-RotorQuant-MLX-4bit")

prompt = "Describe this image in detail."
response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
print(response)

For multimodal usage with images:

from mlx_vlm import load, generate

model, processor = load("majentik/gemma-4-E2B-it-RotorQuant-MLX-4bit")

prompt = "What do you see in this image?"
output = generate(model, processor, prompt=prompt, image="path/to/image.jpg", max_tokens=512)
print(output)

What is RotorQuant?

RotorQuant is a high-performance KV-cache quantization method that achieves significantly better throughput than TurboQuant. Combined with 4-bit weight quantization in MLX, this provides a dual compression strategy with superior KV-cache performance: smaller model weights plus faster compressed KV cache for efficient long-context generation.

Key advantages over TurboQuant:

• 5.3x faster prefill
• 28% faster decode
• Equivalent memory savings

KV-Cache Quantization Comparison

Memory Estimates (Gemma 4 E2B-it)

Hardware Requirements

This model requires approximately 1.2 GB of unified memory. Recommended hardware:

• Apple M1 (8 GB+)
• Apple M2 (8 GB+)
• Apple M3 (8 GB+)
• Apple M4 (8 GB+)
• Any Apple Silicon Mac with 8 GB+ unified memory

See Also

• google/gemma-4-E2B-it -- Base model
• majentik/gemma-4-E2B-it-RotorQuant -- RotorQuant KV-cache only (transformers)
• majentik/gemma-4-E2B-it-RotorQuant-MLX-8bit -- MLX 8-bit variant
• majentik/gemma-4-E2B-it-RotorQuant-MLX-2bit -- MLX 2-bit variant
• majentik/gemma-4-E2B-it-TurboQuant-MLX-4bit -- TurboQuant MLX 4-bit variant
• RotorQuant GitHub
• MLX Framework

Quant trade-off (MLX lane)

(Current variant — 4bit — is bolded.)

Variants in this family

(Showing 14 sibling variants under majentik/gemma-4-e2b-it-*. The current variant — RotorQuant-MLX-4bit — is bolded.)