Gemma 4 26B A4B QAT Styletune v2 Voice — Hardware Requirements & GPU Compatibility
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Gemma 4 26B A4B QAT Styletune v2 Voice is a 26B-parameter open language model from Wiself in the Gemma 4 family. At BF16 it needs about 57.20 GB of VRAM — see which GPUs and Macs can run it below.
Gemma 4 26B A4B QAT Styletune v2 Voice runs on a 64 GB+ Mac or a workstation GPU
Best local pick → Mac Pro M2 Ultra (64 GB) · BF16 · 60.8 GB · ~40 tok/s
Macs from 64 GB (BF16 · ~40 tok/s)
Apache 2.0
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 4 26B A4B QAT Styletune v2 Voice
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
vllm serve Wiself/gemma-4-26B-A4B-QAT-Styletune-V2-Voice --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX PRO 6000 (96 GB) (35 GB headroom) · BF16
- Generation speed
- ~98 tok/s
- generation speed
- Cost per 1M output tokens
- $3.33
- per 1M output tokens
How Much VRAM Does Gemma 4 26B A4B QAT Styletune v2 Voice Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 57.2 GB | 64.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 60.8 GB |
Selected BF16
Brain floating point 1660.8GBat 16K context
- Weights
- 56.4 GB
- KV cache
- 4.1 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 36.8 GB more
Or .
Which Hardware Can Run Gemma 4 26B A4B QAT Styletune v2 Voice?
BF16 · 57.2 GBGemma 4 26B A4B QAT Styletune v2 Voice (BF16) requires 57.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 75+ GB is recommended. No consumer GPU has enough memory. 52 devices with unified memory can run Gemma 4 26B A4B QAT Styletune v2 Voice, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).
64–80 GB
fits · 3.2 GB free · up to 16K ctx
3 Mac classes · 1 device family
M2 Ultra*64 GB~40Simulate output on Mac Pro M2 Ultra (64 GB)M5 Max64 GB~33Simulate output on Mac Studio M5 Max (64 GB)Orin~15Simulate output on NVIDIA Jetson AGX Orin 64GB
By VRAM tier · typical GPUs and Macs at BF16
Alternatives to Gemma 4 26B A4B QAT Styletune v2 Voice
Gemma 4 family · 35 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.8 27BAlibaba | 122.0 | 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 |
| Gemma 3 27B ITGoogle | 79.4 | 21.7 GB | ~36 | same size |
| Gemma 2 27B ITGoogle | 72.7 | 19.5 GB | ~37 | same size |
| Ariarhymes-ai | — | 20.2 GB | ~168 | same size |
Specifications
- Publisher
- Wiself
- Family
- Gemma 4
- Parameters
- 26B
- Active parameters
- 4B per token
- Release Date
- 2026-09-02
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Gemma 4 26B A4B QAT Styletune v2 Voice need?
Gemma 4 26B A4B QAT Styletune v2 Voice requires 57.2 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 26B × 16 bits ÷ 8 = 52 GB
KV Cache + Overhead ≈ 5.2 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.
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run Gemma 4 26B A4B QAT Styletune v2 Voice?
No — Gemma 4 26B A4B QAT Styletune v2 Voice requires at least 57.2 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
Can I run Gemma 4 26B A4B QAT Styletune v2 Voice on a Mac?
Yes — Mac Pro M2 Ultra (64 GB) and 41 other Macs can run Gemma 4 26B A4B QAT Styletune v2 Voice. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 57.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 4 26B A4B QAT Styletune v2 Voice locally?
Yes — Gemma 4 26B A4B QAT Styletune v2 Voice can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 4 26B A4B QAT Styletune v2 Voice?
At BF16, Gemma 4 26B A4B QAT Styletune v2 Voice can reach ~88 tok/s on AMD Instinct MI350X. 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 → 8.8 GB active ÷ (8000 × 0.65) = 1.69 ms, plus 48 layers × 0.055 ms = 2.64 ms, so 1000 ÷ 4.33 ms = ~231 tok/s
Estimated speed at BF16 (57.2 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 QAT Styletune v2 Voice?
At BF16, the download is about 52.00 GB.
Which GPUs can run Gemma 4 26B A4B QAT Styletune v2 Voice?
No single consumer GPU has enough VRAM to run Gemma 4 26B A4B QAT Styletune v2 Voice at BF16 (57.2 GB). Multi-GPU or professional hardware is required.
Which devices can run Gemma 4 26B A4B QAT Styletune v2 Voice?
52 devices with unified memory can run Gemma 4 26B A4B QAT Styletune v2 Voice at BF16 (57.2 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.