Supergemma4 E4b Abliterated — Hardware Requirements & GPU Compatibility
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Supergemma4 E4b Abliterated is a 7.5B-parameter open language model from Jiunsong in the Gemma 4 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 5.03 GB of VRAM — see which GPUs and Macs can run it below.
996 downloads
72 likesSpecifications
- Publisher
- Jiunsong
- Family
- Gemma 4
- Parameters
- 7.5B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-04-17
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Supergemma4 E4b Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 3.7 GB | 17.6 GB | 3.20 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.2 GB | 18.1 GB | 3.67 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 5.0 GB | 18.9 GB | 4.51 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 5.9 GB | 19.8 GB | 5.36 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 6.7 GB | 20.6 GB | 6.20 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 8.0 GB | 21.9 GB | 7.52 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 15.6 GB | 29.4 GB | 15.04 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Supergemma4 E4b Abliterated?
Q4_K_M · 5.0 GBSupergemma4 E4b Abliterated (Q4_K_M) requires 5.0 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 13.9 GB, bringing total usage to 18.9 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Supergemma4 E4b Abliterated?
Q4_K_M · 5.0 GB58 devices with unified memory can run Supergemma4 E4b Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Apple iPhone 17 Pro.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Supergemma4 E4b Abliterated need?
Supergemma4 E4b Abliterated requires 5.0 GB of VRAM at Q4_K_M, or 15.6 GB at BF16. Full 131K context adds up to 13.9 GB (18.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.5B × 4.8 bits ÷ 8 = 4.5 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 14.4 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M5.0 GBQ4_K_M + full context18.9 GB- What's the best quantization for Supergemma4 E4b Abliterated?
For Supergemma4 E4b Abliterated, Q4_K_M (5.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (5.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.7 GB.
VRAM requirement by quantization
Q2_K3.7 GBQ4_K_M ★5.0 GBQ5_K_M5.9 GBQ6_K6.7 GBQ8_08.0 GBBF1615.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Supergemma4 E4b Abliterated on a Mac?
Supergemma4 E4b Abliterated requires at least 3.7 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Supergemma4 E4b Abliterated locally?
Yes — Supergemma4 E4b Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 5.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Supergemma4 E4b Abliterated?
At Q4_K_M, Supergemma4 E4b Abliterated can reach ~875 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~130 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 ÷ 5.0 × 0.65 = ~1034 tok/s
Estimated speed at Q4_K_M (5.0 GB)
~1034 tok/s~130 tok/s~1034 tok/s~875 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Supergemma4 E4b Abliterated?
At Q4_K_M, the download is about 4.51 GB. The full-precision BF16 version is 15.04 GB. The smallest option (Q2_K) is 3.20 GB.
- Which GPUs can run Supergemma4 E4b Abliterated?
50 consumer GPUs can run Supergemma4 E4b Abliterated at Q4_K_M (5.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Supergemma4 E4b Abliterated?
59 devices with unified memory can run Supergemma4 E4b Abliterated at Q4_K_M (5.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.