[!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 - TurboQuant MLX 8-bit

8-bit weight-quantized MLX version of google/gemma-4-E2B-it with the legacy TurboQuant KV-cache fork (superseded by upstream llama.cpp KV options). Optimized for Apple Silicon inference via the MLX framework.

Approximate model size: ~2 GB

Model Specifications

Property	Value
Base Model	google/gemma-4-E2B-it
Parameters	~2 billion
Architecture	Dense transformer
Modality	Multimodal: image + text input, text output
License	Apache 2.0
Weight Quantization	8-bit (~2 GB)
KV-Cache Quantization	TurboQuant
Framework	MLX (Apple Silicon)

Quickstart

python

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

model, tokenizer = load("majentik/gemma-4-E2B-it-TurboQuant-MLX-8bit")

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

For multimodal usage with images:

python

from mlx_vlm import load, generate

model, processor = load("majentik/gemma-4-E2B-it-TurboQuant-MLX-8bit")

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

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).

KV-Cache Quantization Comparison

Method	Prefill Speed	Decode Speed	Memory Savings	Reference
TurboQuant	1x (baseline)	1x (baseline)	High	arXiv: 2504.19874

Memory Estimates (Gemma 4 E2B-it)

Precision	Approximate Size	MLX Variant
FP16 (original)	~4 GB	--
8-bit quantized	~2 GB	This model

Hardware Requirements

This model requires approximately 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

Quant trade-off (MLX lane)

Bits	Approx size	Use case	Recommendation
2-bit	~532 MB	Aggressive quantization	Very low-RAM Macs
3-bit	~737 MB	Lossy but small	Low-RAM Macs
4-bit	~860 MB	Balanced default	Recommended for most Macs
5-bit	~1.0 GB	Higher fidelity	Quality-sensitive
6-bit	~1.2 GB	Approaching FP16 quality	High-fidelity
8-bit	~1.5 GB	Near-lossless reference	Fidelity-critical work

(Current variant — 8bit — is bolded.)

Variants in this family

(Showing 14 sibling variants under majentik/gemma-4-e2b-it-*. The current variant — TurboQuant-MLX-8bit — is bolded.)

Variant	Runtime	Approx size	Use case
RotorQuant-GGUF-IQ4_XS	llama.cpp	~1.7 GB	Lossy 4-bit, low-RAM CPU/edge
RotorQuant-GGUF-Q2_K	llama.cpp	~1.2 GB	Lossy, low-RAM CPU/edge
RotorQuant-GGUF-Q3_K_M	llama.cpp	~1.6 GB	Smaller 3-bit, CPU-friendly
RotorQuant-GGUF-Q4_K_M	llama.cpp	~2.2 GB	Balanced default
RotorQuant-GGUF-Q5_K_M	llama.cpp	~2.6 GB	Higher fidelity, more RAM
RotorQuant-GGUF-Q8_0	llama.cpp	~4.2 GB	Near-lossless reference
RotorQuant-MLX-2bit	mlx-lm	~655 MB	Apple Silicon, smallest
RotorQuant-MLX-4bit	mlx-lm	~1.2 GB	Apple Silicon balanced
TurboQuant-MLX-8bit	mlx-lm	~2.4 GB	Apple Silicon reference

majentik/gemma-4-E2B-it-TurboQuant-MLX-8bit