Gemma 4 26B A4B StyleTune v2 — Hardware Requirements & GPU Compatibility
ChatRoleplay
Gemma 4 26B A4B StyleTune v2 is a 26.5B-parameter open language model from Gryphe in the Gemma 4 family. It supports a context window of up to 262,144 tokens. At BF16 it needs about 53.73 GB of VRAM — see which GPUs and Macs can run it below.
Gemma 4 26B A4B StyleTune v2 runs on a 64 GB+ Mac or a workstation GPU
Best local pick → Mac Pro M2 Ultra (64 GB) · BF16 · 54.1 GB · ~41 tok/s
Macs from 64 GB (BF16 · ~41 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 StyleTune v2
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve Gryphe/Gemma-4-26B-A4B-StyleTune-V2 --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 PRO 6000 (96 GB) (41 GB headroom) · BF16
- Generation speed
- ~95 tok/s
- generation speed
- Cost per 1M output tokens
- $3.93
- per 1M output tokens
How Much VRAM Does Gemma 4 26B A4B StyleTune v2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 53.7 GB | 54.6 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 54.1 GB |
Selected BF16
Brain floating point 1654.1GBat 16K context
- Weights
- 53.1 GB
- KV cache
- 0.7 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 30.1 GB more
Which Hardware Can Run Gemma 4 26B A4B StyleTune v2?
BF16 · 53.7 GBGemma 4 26B A4B StyleTune v2 (BF16) requires 53.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 70+ GB is recommended. Using the full 262K context window can add up to 8.7 GB, bringing total usage to 62.4 GB. No consumer GPU has enough memory. 52 devices with unified memory can run Gemma 4 26B A4B StyleTune v2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).
64–80 GB
fits · 9.9 GB free · up to 256K ctx
3 Mac classes · 1 device family
M2 Ultra*64 GB~41Simulate output on Mac Pro M2 Ultra (64 GB)M5 Max64 GB~33Simulate output on Mac Studio M5 Max (64 GB)Orin~13Simulate output on NVIDIA Jetson AGX Orin 64GB
By VRAM tier · typical GPUs and Macs at BF16
Alternatives to Gemma 4 26B A4B StyleTune v2
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 |
| 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 |
Fine-tunes & variants(1)
Specifications
- Publisher
- Gryphe
- Family
- Gemma 4
- Parameters
- 26.5B
- Active parameters
- 5.1B per token
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-06-20
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Gemma 4 26B A4B StyleTune v2 need?
Gemma 4 26B A4B StyleTune v2 requires 53.7 GB of VRAM at BF16. Full 262K context adds up to 8.7 GB (62.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 26.5B × 16 bits ÷ 8 = 53.1 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 5090 run Gemma 4 26B A4B StyleTune v2?
No — Gemma 4 26B A4B StyleTune v2 requires at least 53.7 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
Can I run Gemma 4 26B A4B StyleTune v2 on a Mac?
Yes — Mac Pro M2 Ultra (64 GB) and 41 other Macs can run Gemma 4 26B A4B StyleTune v2. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 53.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 4 26B A4B StyleTune v2 locally?
Yes — Gemma 4 26B A4B StyleTune v2 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 4 26B A4B StyleTune v2?
At BF16, Gemma 4 26B A4B StyleTune v2 can reach ~123 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 → 10.4 GB active ÷ (8000 × 0.65) = 2.00 ms, plus 30 layers × 0.055 ms = 1.65 ms, so 1000 ÷ 3.65 ms = ~274 tok/s
Estimated speed at BF16 (53.7 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 StyleTune v2?
At BF16, the download is about 53.09 GB.
Which GPUs can run Gemma 4 26B A4B StyleTune v2?
No single consumer GPU has enough VRAM to run Gemma 4 26B A4B StyleTune v2 at BF16 (53.7 GB). Multi-GPU or professional hardware is required.
Which devices can run Gemma 4 26B A4B StyleTune v2?
52 devices with unified memory can run Gemma 4 26B A4B StyleTune v2 at BF16 (53.7 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.