Gemma4 E2b Abliterlitics — Hardware Requirements & GPU Compatibility
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Gemma4 E2b Abliterlitics is a 2B-parameter open language model from DreamFast in the Gemma 4 family. At BF16 it needs about 4.40 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 16K)Best on 24 GB (BF16 · ~149 tok/s · 32K)Macs from 8 GB (BF16 · ~16 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma4 E2b Abliterlitics
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve DreamFast/Gemma4-e2b-abliterlitics --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (5 GB headroom) · BF16
- Generation speed
- ~135 tok/s
- generation speed
- Cost per 1M output tokens
- $0.25
- per 1M output tokens
How Much VRAM Does Gemma4 E2b Abliterlitics Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.4 GB | 8.1 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~149 tok/s |
Selected BF16
Brain floating point 166.1GBat 16K context
- Weights
- 3.9 GB
- KV cache
- 2.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.9 GB free · ~149 tok/s
≥50 tok/s needs ≥338 GB/s · RTX 5090 ~265, RTX 3090 Ti ~149
Which Hardware Can Run Gemma4 E2b Abliterlitics?
BF16 · 4.4 GBGemma4 E2b Abliterlitics (BF16) requires 4.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Gemma4 E2b Abliterlitics, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 1.9 GB free · up to 16K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~112Simulate output on RTX 3080RTX 2080 Ti~91Simulate output on RTX 2080 TiRTX 3070 Ti~90Simulate output on RTX 3070 TiM3*8 GB~16Simulate output on iMac 24" M3 (8 GB)Orin Nano~15Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~11Simulate output on iPhone 1720 GB
fits · 13.9 GB free · up to 32K ctx
1 GPU
RX 7900 XT~109Simulate output on RX 7900 XT32–40 GB
fits · 25.9 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~265Simulate output on RTX 5090M5 Max36 GB~73Simulate output on Mac Studio M5 Max (36 GB)M532 GB~24Simulate output on MacBook Air 13" M5 (32 GB)Orin~30Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~20Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Alternatives to Gemma4 E2b Abliterlitics
Gemma 4 family · 35 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| D1 2BLiquid AI | — | 2.3 GB | ~423 | same size |
| LFM3 VL 2BLiquid AI | — | 2.3 GB | ~423 | same size |
| Qwen3 VL 2B InstructAlibaba | — | 3.5 GB | ~362 | same size |
| Moondream2vikhyatk | — | 3 GB | ~516 | same size |
| OneRec 1.7BOpenOneRec | — | 3.5 GB | ~362 | same size |
| Gemma 3n E2B IT Litert LmGoogle | — | 3.1 GB | ~496 | same size |
Specifications
- Publisher
- DreamFast
- Family
- Gemma 4
- Parameters
- 2B
- Release Date
- 2026-05-23
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Gemma4 E2b Abliterlitics need?
Gemma4 E2b Abliterlitics requires 4.4 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2B × 16 bits ÷ 8 = 4 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can I run Gemma4 E2b Abliterlitics on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Gemma4 E2b Abliterlitics. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 4.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma4 E2b Abliterlitics locally?
Yes — Gemma4 E2b Abliterlitics can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma4 E2b Abliterlitics?
At BF16, Gemma4 E2b Abliterlitics can reach ~1091 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~149 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 4.4 × 0.65 = ~1182 tok/s
Estimated speed at BF16 (4.4 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma4 E2b Abliterlitics?
At BF16, the download is about 4.00 GB.
Which GPUs can run Gemma4 E2b Abliterlitics?
52 consumer GPUs can run Gemma4 E2b Abliterlitics at BF16. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Gemma4 E2b Abliterlitics?
163 devices with unified memory can run Gemma4 E2b Abliterlitics at BF16 (4.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.