Gemma 4 26B A4B IT Uncensored — Hardware Requirements & GPU Compatibility
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Gemma 4 26B A4B IT Uncensored is a 25.8B-parameter open language model from TrevorJS in the Gemma 4 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 16.13 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (Q2_K · 16K)Best on 24 GB (Q4_K_M · ~171 tok/s · 128K)Macs from 16 GB (Q2_K · ~50 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 4 26B A4B IT Uncensored
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/TrevorJS/gemma-4-26B-A4B-it-uncensored-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 7.5 GB free · 16K context
Download: gemma-4-26B-A4B-it-uncensored-Q4_K_M.gguf16.80 GBTrevorJS
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3090 (24 GB) (7 GB headroom) · Q4_K_M
- Generation speed
- ~162 tok/s
- generation speed
- Cost per 1M output tokens
- $0.37
- per 1M output tokens
How Much VRAM Does Gemma 4 26B A4B IT Uncensored Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 11.6 GB | 12.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~214 tok/s | |
| 13.2 GB | 14.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~197 tok/s | |
| 16.1 GB | 17 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~171 tok/s | |
| 19.0 GB | 19.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~152 tok/s | |
| 21.9 GB | 22.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 64K context · ~136 tok/s | |
| 26.4 GB | 27.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 26.8 GB | |
| 52.3 GB | 53.2 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 52.6 GB |
Selected Q4_K_M
4-bit medium quantization16.5GBat 16K context
- Weights
- 15.5 GB
- KV cache
- 0.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 7.5 GB free · ~171 tok/s
≥50 tok/s needs ≥230 GB/s · RTX 5090 ~250, RTX 3090 Ti ~171
Which Hardware Can Run Gemma 4 26B A4B IT Uncensored?
Q4_K_M · 16.1 GBGemma 4 26B A4B IT Uncensored (Q4_K_M) requires 16.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 21+ GB is recommended. Using the full 262K context window can add up to 8.7 GB, bringing total usage to 24.8 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Gemma 4 26B A4B IT Uncensored, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).
20 GB
fits · 3.5 GB free · up to 64K ctx
1 GPU
RX 7900 XT~85Simulate output on RX 7900 XT32–40 GB
fits · 15.5 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~250Simulate output on RTX 5090M5 Max36 GB~69Simulate output on Mac Studio M5 Max (36 GB)M532 GB~32Simulate output on MacBook Air 13" M5 (32 GB)Orin~45Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~29Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Gemma 4 26B A4B IT Uncensored
Gemma 4 family · 35 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.8 27BAlibaba | 122.1 | 17.9 GB | ~38 | same size |
| Qwen3.6 27BAlibaba | 111.2 | 17.9 GB | ~38 | same size |
| Qwen3.5 27BAlibaba | 103.8 | 17.9 GB | ~38 | same size |
| Magistral Small 2509Mistral AI | 85.6 | 18.1 GB | ~43 | same size |
| Gemma 3 27B ITGoogle | 79.4 | 21.7 GB | ~36 | same size |
| LFM2.5 24B A2BLiquid AI | — | 15 GB | ~221 | same size |
Specifications
- Publisher
- TrevorJS
- Family
- Gemma 4
- Parameters
- 25.8B
- Active parameters
- 4.4B per token
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-04-03
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Gemma 4 26B A4B IT Uncensored need?
Gemma 4 26B A4B IT Uncensored requires 16.1 GB of VRAM at Q4_K_M, or 52.3 GB at BF16. Full 262K context adds up to 8.7 GB (24.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 25.8B × 4.8 bits ÷ 8 = 15.5 GB
KV Cache + Overhead ≈ 0.6 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 ≈ 9.3 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Gemma 4 26B A4B IT Uncensored?
Yes, at Q6_K (21.9 GB) or lower. Higher quantizations like Q8_0 (26.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Gemma 4 26B A4B IT Uncensored?
For Gemma 4 26B A4B IT Uncensored, Q4_K_M (16.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (19.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 11.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 4 26B A4B IT Uncensored on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Gemma 4 26B A4B IT Uncensored, 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 16.1 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).
Can I run Gemma 4 26B A4B IT Uncensored locally?
Yes — Gemma 4 26B A4B IT Uncensored can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 4 26B A4B IT Uncensored?
At Q4_K_M, Gemma 4 26B A4B IT Uncensored can reach ~152 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~171 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 2.7 GB active ÷ (8000 × 0.65) = 0.53 ms, plus 30 layers × 0.055 ms = 1.65 ms, so 1000 ÷ 2.18 ms = ~459 tok/s
Estimated speed at Q4_K_M (16.1 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma 4 26B A4B IT Uncensored?
At Q4_K_M, the download is about 15.48 GB. The full-precision BF16 version is 51.61 GB. The smallest option (Q2_K) is 10.97 GB.
Which GPUs can run Gemma 4 26B A4B IT Uncensored?
8 consumer GPUs can run Gemma 4 26B A4B IT Uncensored at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Gemma 4 26B A4B IT Uncensored?
122 devices with unified memory can run Gemma 4 26B A4B IT Uncensored at Q4_K_M (16.1 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.