Gemma 2 2B IT Abliterated — Hardware Requirements & GPU Compatibility
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Gemma 2 2B IT Abliterated is a 2.6B-parameter open language model from IlyaGusev in the Gemma 2 family. It supports a context window of up to 8,192 tokens. At Q4_K_M it needs about 2.11 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 · 8K)Best on 24 GB (Q4_K_M · ~311 tok/s · 8K)Macs from 8 GB (Q4_K_M · ~34 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 2 2B IT Abliterated
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/bartowski/gemma-2-2b-it-abliterated-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 21.4 GB free · 8K context
Download: gemma-2-2b-it-abliterated-Q4_K_M.gguf1.71 GBbartowski
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (9 GB headroom) · Q4_K_M
- Generation speed
- ~111 tok/s
- generation speed
- Cost per 1M output tokens
- $0.19
- per 1M output tokens
How Much VRAM Does Gemma 2 2B IT Abliterated Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.6 GB | — | 3-bit · i-quant extra small — Importance-weighted 3-bit, extra small | Fits: 8K context · ~404 tok/s | |
| 1.7 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 8K context · ~395 tok/s | |
| 1.7 GB | — | 3-bit small — 3-bit small quantization | Fits: 8K context · ~388 tok/s | |
| 1.8 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 8K context · ~360 tok/s | |
| 2.1 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~311 tok/s | |
| 2.4 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 8K context · ~272 tok/s | |
| 2.7 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 8K context · ~243 tok/s | |
| 3.2 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 8K context · ~207 tok/s |
Selected Q4_K_M
4-bit medium quantization2.6GBat 8K context
- Weights
- 1.6 GB
- KV cache
- 0.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.4 GB free · ~311 tok/s
≥50 tok/s needs ≥162 GB/s · RTX 5090 ~552, RTX 3090 Ti ~311
Which Hardware Can Run Gemma 2 2B IT Abliterated?
Q4_K_M · 2.1 GBGemma 2 2B IT Abliterated (Q4_K_M) requires 2.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 8K context window can add up to 0.5 GB, bringing total usage to 2.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Gemma 2 2B IT Abliterated, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.4 GB free · up to 8K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~234Simulate output on RTX 3080RTX 2080 Ti~190Simulate output on RTX 2080 TiRTX 3070 Ti~187Simulate output on RTX 3070 TiM3*8 GB~34Simulate output on iMac 24" M3 (8 GB)Orin Nano~31Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~23Simulate output on iPhone 1720 GB
fits · 17.4 GB free · up to 8K ctx
1 GPU
RX 7900 XT~228Simulate output on RX 7900 XT32–40 GB
fits · 29.4 GB free · up to 8K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~552Simulate output on RTX 5090M5 Max36 GB~153Simulate output on Mac Studio M5 Max (36 GB)M532 GB~51Simulate output on MacBook Air 13" M5 (32 GB)Orin~63Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~42Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Gemma 2 2B IT Abliterated
Gemma 2 family · 20 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM5 2BOpenBMB | — | 2.5 GB | ~345 | same size |
| North Micro Vision InstructCohere | — | 7.1 GB | ~119 | same size |
| LFM2.5 2.6BLiquid AI | — | 2.3 GB | ~321 | same size |
| Granite 3.1 2B InstructIBM | — | 3.2 GB | ~329 | same size |
| Phi 2Microsoft | — | 2.6 GB | ~248 | same size |
| Ouro 2.6B ThinkingByteDance | — | 12.1 GB | ~102 | same size |
Specifications
- Publisher
- IlyaGusev
- Family
- Gemma 2
- Parameters
- 2.6B
- Architecture
- Gemma2ForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 256,000
- Release Date
- 2024-07-31
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Gemma 2 2B IT Abliterated need?
Gemma 2 2B IT Abliterated requires 2.1 GB of VRAM at Q4_K_M, or 5.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.6B × 4.8 bits ÷ 8 = 1.6 GB
KV Cache + Overhead ≈ 0.5 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 8K context)
VRAM usage by quantization
What's the best quantization for Gemma 2 2B IT Abliterated?
For Gemma 2 2B IT Abliterated, Q4_K_M (2.1 GB) offers the best balance of quality and VRAM usage. Q4_K_L (2.1 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 1.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 2 2B IT Abliterated on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Gemma 2 2B IT Abliterated. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 2 2B IT Abliterated locally?
Yes — Gemma 2 2B IT Abliterated can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 2 2B IT Abliterated?
At Q4_K_M, Gemma 2 2B IT Abliterated can reach ~2275 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~311 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 ÷ 2.1100 × 0.65 = ~2464 tok/s
Estimated speed at Q4_K_M (2.1 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma 2 2B IT Abliterated?
At Q4_K_M, the download is about 1.57 GB. The full-precision BF16 version is 5.23 GB. The smallest option (IQ3_XS) is 1.08 GB.
Which GPUs can run Gemma 2 2B IT Abliterated?
52 consumer GPUs can run Gemma 2 2B IT 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 2 2B IT Abliterated?
163 devices with unified memory can run Gemma 2 2B IT Abliterated at Q4_K_M (2.1 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.