Gemma 3 1B IT Heretic Extreme Uncensored Abliterated — Hardware Requirements & GPU Compatibility
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Gemma 3 1B IT Heretic Extreme Uncensored Abliterated is a 1000M-parameter open language model from DavidAU in the Gemma 3 family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 0.96 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 · 32K)Best on 24 GB (Q4_K_M · ~683 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~75 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 3 1B IT Heretic Extreme Uncensored Abliterated
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-3-1b-it-heretic-extreme-uncensored-abliterated-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 23.0 GB free · 16K context
Download: gemma-3-1b-it-heretic-extreme-uncensored-abliterated.i1-Q4_K_M.gguf0.81 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~244 tok/s
- generation speed
- Cost per 1M output tokens
- $0.068
- per 1M output tokens
How Much VRAM Does Gemma 3 1B IT Heretic Extreme Uncensored Abliterated Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.8 GB | 1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~829 tok/s | |
| 0.8 GB | 1 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~771 tok/s | |
| 1.0 GB | 1.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~683 tok/s | |
| 1.1 GB | 1.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~612 tok/s | |
| 1.2 GB | 1.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~551 tok/s | |
| 1.4 GB | 1.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~482 tok/s | |
| 2.4 GB | 2.6 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~278 tok/s |
Selected Q4_K_M
4-bit medium quantization1.0GBat 16K context
- Weights
- 0.6 GB
- KV cache
- 0.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.0 GB free · ~683 tok/s
≥50 tok/s needs ≥74 GB/s · RTX 5090 ~1213, RTX 3090 Ti ~683
Which Hardware Can Run Gemma 3 1B IT Heretic Extreme Uncensored Abliterated?
Q4_K_M · 1.0 GBGemma 3 1B IT Heretic Extreme Uncensored Abliterated (Q4_K_M) requires 1.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 33K context window can add up to 0.2 GB, bringing total usage to 1.1 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Gemma 3 1B IT Heretic Extreme Uncensored Abliterated, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.0 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~515Simulate output on RTX 3080RTX 2080 Ti~417Simulate output on RTX 2080 TiRTX 3070 Ti~412Simulate output on RTX 3070 TiM3*8 GB~75Simulate output on iMac 24" M3 (8 GB)Orin Nano~69Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~50Simulate output on iPhone 1720 GB
fits · 19.0 GB free · up to 32K ctx
1 GPU
RX 7900 XT~500Simulate output on RX 7900 XT32–40 GB
fits · 31.0 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1213Simulate output on RTX 5090M5 Max36 GB~335Simulate output on Mac Studio M5 Max (36 GB)M532 GB~112Simulate output on MacBook Air 13" M5 (32 GB)Orin~139Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~91Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Gemma 3 1B IT Heretic Extreme Uncensored Abliterated
Gemma 3 family · 20 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| InternVL3 5 1BOpenGVLab | — | 2.2 GB | ~936 | same size |
| InternVL3 1BOpenGVLab | — | 2 GB | ~1057 | same size |
| InternVL2 1BOpenGVLab | — | 2 GB | ~1057 | same size |
| DiffuRefill 1BAsilarkness | — | 2.6 GB | ~252 | same size |
| Crowd V1Vxtzq | — | 3.7 GB | ~295 | same size |
| LLaMA3.1 8B Instruct DFlash UltraChatz-lab | — | 2.7 GB | ~269 | same size |
Specifications
- Publisher
- DavidAU
- Family
- Gemma 3
- Parameters
- 1000M
- Architecture
- Gemma3ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2025-11-20
Frequently Asked Questions
How much VRAM does Gemma 3 1B IT Heretic Extreme Uncensored Abliterated need?
Gemma 3 1B IT Heretic Extreme Uncensored Abliterated requires 1.0 GB of VRAM at Q4_K_M, or 2.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1000M × 4.8 bits ÷ 8 = 0.6 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.
KV Cache + Overhead ≈ 0.5 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for Gemma 3 1B IT Heretic Extreme Uncensored Abliterated?
For Gemma 3 1B IT Heretic Extreme Uncensored Abliterated, Q4_K_M (1.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 3 1B IT Heretic Extreme Uncensored Abliterated on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Gemma 3 1B IT Heretic Extreme Uncensored Abliterated. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 3 1B IT Heretic Extreme Uncensored Abliterated locally?
Yes — Gemma 3 1B IT Heretic Extreme Uncensored Abliterated can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 3 1B IT Heretic Extreme Uncensored Abliterated?
At Q4_K_M, Gemma 3 1B IT Heretic Extreme Uncensored Abliterated can reach ~5000 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~683 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 ÷ 0.96 × 0.65 = ~5417 tok/s
Estimated speed at Q4_K_M (1.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma 3 1B IT Heretic Extreme Uncensored Abliterated?
At Q4_K_M, the download is about 0.60 GB. The full-precision BF16 version is 2.00 GB. The smallest option (Q2_K) is 0.42 GB.
Which GPUs can run Gemma 3 1B IT Heretic Extreme Uncensored Abliterated?
52 consumer GPUs can run Gemma 3 1B IT Heretic Extreme Uncensored Abliterated 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 3 1B IT Heretic Extreme Uncensored Abliterated?
163 devices with unified memory can run Gemma 3 1B IT Heretic Extreme Uncensored Abliterated at Q4_K_M (1.0 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.