Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic — Hardware Requirements & GPU Compatibility
CodeGemma 4 E2B IT Qat Q4 0 Unquantized Heretic is a 5.1B-parameter open language model from coder3101 in the Gemma 4 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 3.42 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 128K)Best on 24 GB (Q4_K_M · ~192 tok/s · 128K)Macs from 8 GB (Q4_K_M · ~21 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic
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
ollama run hf.co/mradermacher/gemma-4-E2B-it-qat-q4_0-unquantized-heretic-GGUFQ4_K_M fits fully on the RTX 4090 · 20.5 GB free · 16K context
Download: gemma-4-E2B-it-qat-q4_0-unquantized-heretic.Q4_K_M.gguf3.42 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~68 tok/s
- generation speed
- Cost per 1M output tokens
- $0.24
- per 1M output tokens
How Much VRAM Does Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.5 GB | 2.7 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~260 tok/s | |
| 2.8 GB | 3 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~231 tok/s | |
| 2.9 GB | 3.1 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 128K context · ~225 tok/s | |
| 3.4 GB | 3.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~192 tok/s | |
| 4.0 GB | 4.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~164 tok/s | |
| 4.6 GB | 4.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~143 tok/s | |
| 5.5 GB | 5.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~120 tok/s | |
| 10.6 GB | 10.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 128K context · ~62 tok/s |
Selected Q4_K_M
4-bit medium quantization3.5GBat 16K context
- Weights
- 3.1 GB
- KV cache
- 0.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.5 GB free · ~192 tok/s
≥50 tok/s needs ≥263 GB/s · RTX 5090 ~341, RTX 3090 Ti ~192
Which Hardware Can Run Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic?
Q4_K_M · 3.4 GBGemma 4 E2B IT Qat Q4 0 Unquantized Heretic (Q4_K_M) requires 3.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 131K context window can add up to 0.7 GB, bringing total usage to 4.1 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.5 GB free · up to 128K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~145Simulate output on RTX 3080RTX 2080 Ti~117Simulate output on RTX 2080 TiRTX 3070 Ti~116Simulate output on RTX 3070 TiM3*8 GB~21Simulate output on iMac 24" M3 (8 GB)Orin Nano~19Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~14Simulate output on iPhone 1720 GB
fits · 16.5 GB free · up to 128K ctx
1 GPU
RX 7900 XT~140Simulate output on RX 7900 XT32–40 GB
fits · 28.5 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~341Simulate output on RTX 5090M5 Max36 GB~94Simulate output on Mac Studio M5 Max (36 GB)M532 GB~31Simulate output on MacBook Air 13" M5 (32 GB)Orin~39Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~26Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic
Gemma 4 family · 35 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| WeVisOne VL 4BTencent | — | 4.7 GB | ~193 | same size |
| Youtu Parsing OmniTencent | — | 12.7 GB | ~59 | same size |
| Molmo2 4BAllen AI | — | 4.7 GB | ~193 | same size |
| Gemma 3n E2B ITGoogle | — | 5.9 GB | ~183 | same size |
| Xflux Text EncodersXLabs-AI | — | 12.7 GB | ~63 | same size |
| K2 Horizon 3.7BIFM | — | 11.9 GB | ~62 | same size |
Specifications
- Publisher
- coder3101
- Family
- Gemma 4
- Parameters
- 5.1B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-06-06
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic need?
Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic requires 3.4 GB of VRAM at Q4_K_M, or 10.6 GB at BF16. Full 131K context adds up to 0.7 GB (4.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 5.1B × 4.8 bits ÷ 8 = 3.1 GB
KV Cache + Overhead ≈ 0.3 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.
KV Cache + Overhead ≈ 1 GB (at full 131K context)
VRAM usage by quantization
What's the best quantization for Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic?
For Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic, Q4_K_M (3.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (4.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic locally?
Yes — Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic?
At Q4_K_M, Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic can reach ~1404 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~192 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 ÷ 3.4200 × 0.65 = ~1520 tok/s
Estimated speed at Q4_K_M (3.4 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic?
At Q4_K_M, the download is about 3.06 GB. The full-precision BF16 version is 10.21 GB. The smallest option (Q2_K) is 2.17 GB.
Which GPUs can run Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic?
52 consumer GPUs can run Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic at Q4_K_M. 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 Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic?
163 devices with unified memory can run Gemma 4 E2B IT Qat Q4 0 Unquantized Heretic at Q4_K_M (3.4 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.