Gemma 4 E4B IT OBLITERATED — Hardware Requirements & GPU Compatibility
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Gemma 4 E4B IT OBLITERATED is a 8.0B-parameter open language model from OBLITERATUS in the Gemma 4 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 5.32 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 · 64K)Best on 24 GB (Q4_K_M · ~123 tok/s · 128K)Macs from 8 GB (Q2_K · ~18 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 4 E4B IT OBLITERATED
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve OBLITERATUS/gemma-4-E4B-it-OBLITERATED --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (6 GB headroom) · Q4_K_M
- Generation speed
- ~44 tok/s
- generation speed
- Cost per 1M output tokens
- $0.44
- per 1M output tokens
How Much VRAM Does Gemma 4 E4B IT OBLITERATED Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.9 GB | 4.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~167 tok/s | |
| 4.4 GB | 5 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~148 tok/s | |
| 5.3 GB | 5.9 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~123 tok/s | |
| 6.2 GB | 6.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~105 tok/s | |
| 7.1 GB | 7.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~92 tok/s | |
| 8.5 GB | 9.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~77 tok/s | |
| 16.5 GB | 17.1 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 128K context · ~40 tok/s |
Selected Q4_K_M
4-bit medium quantization5.6GBat 16K context
- Weights
- 4.8 GB
- KV cache
- 0.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 18.4 GB free · ~123 tok/s
≥50 tok/s needs ≥409 GB/s · RTX 5090 ~219, RTX 3090 Ti ~123
Which Hardware Can Run Gemma 4 E4B IT OBLITERATED?
Q4_K_M · 5.3 GBGemma 4 E4B IT OBLITERATED (Q4_K_M) requires 5.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 131K context window can add up to 2.9 GB, bringing total usage to 8.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Gemma 4 E4B IT OBLITERATED, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 2.4 GB free · up to 64K ctx
16 GPUs · 1 device family
RTX 3080~93Simulate output on RTX 3080RTX 2080 Ti~75Simulate output on RTX 2080 TiRTX 3070 Ti~74Simulate output on RTX 3070 TiOrin Nano~13Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 14.4 GB free · up to 128K ctx
1 GPU
RX 7900 XT~90Simulate output on RX 7900 XT32–40 GB
fits · 26.4 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~219Simulate output on RTX 5090M5 Max36 GB~61Simulate output on Mac Studio M5 Max (36 GB)M532 GB~20Simulate output on MacBook Air 13" M5 (32 GB)Orin~25Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~17Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Gemma 4 E4B IT OBLITERATED
Gemma 4 family · 35 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
| InternVL3 5 8BOpenGVLab | — | 8.2 GB | ~116 | same size |
Specifications
- Publisher
- OBLITERATUS
- Family
- Gemma 4
- Parameters
- 8.0B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-04-15
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Gemma 4 E4B IT OBLITERATED need?
Gemma 4 E4B IT OBLITERATED requires 5.3 GB of VRAM at Q4_K_M, or 16.5 GB at BF16. Full 131K context adds up to 2.9 GB (8.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 4.8 bits ÷ 8 = 4.8 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 ≈ 3.4 GB (at full 131K context)
VRAM usage by quantization
What's the best quantization for Gemma 4 E4B IT OBLITERATED?
For Gemma 4 E4B IT OBLITERATED, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.9 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 4 E4B IT OBLITERATED on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Gemma 4 E4B IT OBLITERATED, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.3 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run Gemma 4 E4B IT OBLITERATED locally?
Yes — Gemma 4 E4B IT OBLITERATED can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 4 E4B IT OBLITERATED?
At Q4_K_M, Gemma 4 E4B IT OBLITERATED can reach ~902 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~123 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 ÷ 5.32 × 0.65 = ~977 tok/s
Estimated speed at Q4_K_M (5.3 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 E4B IT OBLITERATED?
At Q4_K_M, the download is about 4.80 GB. The full-precision BF16 version is 15.99 GB. The smallest option (Q2_K) is 3.40 GB.
Which GPUs can run Gemma 4 E4B IT OBLITERATED?
52 consumer GPUs can run Gemma 4 E4B IT OBLITERATED 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 E4B IT OBLITERATED?
151 devices with unified memory can run Gemma 4 E4B IT OBLITERATED at Q4_K_M (5.3 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.