Gemma 4 is Google's most capable open model family to date, and if the search traffic on OwnRig is any signal, thousands of people are trying to figure out the same thing right now: which GPU actually runs it?
The honest answer is: it depends which variant you want. The family spans from tiny edge models to a 31B behemoth that will stress most consumer GPUs. Here's exactly what each one needs, with no hedging.
The Gemma 4 family, explained plainly
Google released four Gemma 4 variants in April 2026. They look like a simple lineup, but the naming trips people up. Here's what you're actually choosing between:
| Model | Total params | Architecture | Min VRAM | Recommended VRAM |
|---|---|---|---|---|
| Gemma 4 E2B | 5.1B | Dense | 4 GB | 4.5–5.5 GB |
| Gemma 4 E4B | 8B | Dense | 6 GB | 7 GB |
| Gemma 4 26B-A4B | 25.2B | MoE (3.8B active) | 14 GB | 20.5–24 GB |
| Gemma 4 31B | 30.7B | Dense | 16 GB | 24–28 GB |
The confusing one is the 26B-A4B. The "A4B" means only 4 billion parameters are active per token, but it's a Mixture of Experts model: all 26 billion parameters still need to fit in VRAM, because the routing layer picks different experts for each token. Don't let "4B active" fool you into thinking this runs like a 4B model. It doesn't.
Gemma 4 E2B and E4B: the easy ones
If you want to run Gemma 4 and your GPU has 8 GB or more of VRAM, the E2B and E4B variants run without drama on almost any modern hardware. These are dense models in the 5 to 8 billion parameter range.
They're fast. They fit. They're Google's answer to Phi-4 Mini and Gemma 3 4B. If you need a local AI that runs on a laptop GPU or an older gaming card and handles most everyday tasks, start here.
Gemma 4 26B-A4B: where things get interesting
This is the model everyone wants to run, and the one causing the most confusion. Here are the numbers from our database, with honest calls on which hardware actually works:
18 GB
VRAM needed for Gemma 4 26B-A4B at Q4_K_M
The recommended quantization for this model at consumer VRAM levels
| Quantization | VRAM needed | Fits in | Quality |
|---|---|---|---|
| Q8_0 | 28 GB | RTX 5090 (32 GB), M4 Max 36 GB+ | Full quality |
| Q6_K | 24 GB | RTX 4090 (24 GB, tight), M4 Pro 24 GB+ | Near-full quality |
| Q5_K_M | 20.5 GB | RTX 4090 (with headroom), M4 Pro 24 GB | Excellent |
| Q4_K_M | 18 GB | RTX 4090, M4 Pro 24 GB | Efficient tier in our model data |
| Q3_K_M | 14 GB | RTX 4060 Ti 16 GB | Compressed tier in our model data |
The RTX 4060 Ti 16 GB can run the 26B-A4B, but only at Q3_K_M, which is where you start noticing quality gaps on harder reasoning tasks. It's not unusable, but you're getting a compromised experience. The honest recommendation: the 26B-A4B wants more than 20 GB for the recommended tiers in our data. The RTX 4090 is the minimum consumer GPU I'd pair with this model if quality matters.
Gemma 4 31B: the VRAM-hungry one
The 31B is a dense model, not MoE. All 30.7 billion parameters are active for every token. That means it's slower than the 26B-A4B but with more consistent reasoning quality. And it needs real hardware.
| Quantization | VRAM needed | Fits in |
|---|---|---|
| Q8_0 | 34 GB | M4 Max 36 GB+, multi-GPU setups |
| Q6_K | 28 GB | M4 Max 36 GB+ (with headroom) |
| Q5_K_M | 24 GB | RTX 4090 (tight), M4 Pro 24 GB |
| Q4_K_M | 21 GB | RTX 4090 (comfortable), M4 Pro 24 GB |
| Q3_K_M | 16 GB | RTX 4060 Ti 16 GB (barely; degraded quality) |
Q4_K_M at 21 GB is the target for most people. An RTX 4090 handles it with about 3 GB of raw memory headroom. An M4 Pro 24 GB is also a verified Q4_K_M fit in the compatibility matrix, though at a lower performance tier than the 4090.
GPU compatibility at a glance
Based on our compatibility data, here's the honest per-GPU verdict for the 26B-A4B and 31B variants:
| GPU | VRAM | 26B-A4B verdict | 31B verdict |
|---|---|---|---|
| RTX 4060 8GB | 8 GB | No (too little VRAM) | No (too little VRAM) |
| RTX 4060 Ti 16GB | 16 GB | Q3_K_M only (degraded) | Q3_K_M only (degraded) |
| RTX 4070 Ti 12GB | 12 GB | No | No |
| RTX 4070 Ti Super | 16 GB | Q3_K_M (degraded) | Q3_K_M (degraded) |
| RTX 4090 | 24 GB | Q4_K_M to Q6_K (good) | Q4_K_M (comfortable) |
| RTX 5080 | 16 GB | Q3_K_M (degraded) | Q3_K_M (degraded) |
| RTX 5090 | 32 GB | Q6_K to Q8 (excellent) | Q5_K_M to Q6_K (excellent) |
| M4 (16GB Unified) | 16 GB | Q3_K_M (tight) | No |
| M4 Pro (24GB Unified) | 24 GB | Q4_K_M (good) | Q4_K_M (comfortable) |
| M4 Pro (48GB) | 48 GB | Q8 (full quality) | Q6_K to Q8 (excellent) |
Apple Silicon vs Nvidia: the honest comparison
Apple Silicon has a structural advantage for Gemma 4: unified memory. A Mac with 24 GB doesn't split that between CPU and GPU; the AI model gets all of it. An M4 Pro 24 GB and an RTX 4090 24 GB land in roughly the same place for Gemma 4 model compatibility.
The speed is different. The RTX 4090 will be faster on tokens-per-second benchmarks for the 26B-A4B due to raw compute throughput. But the M4 Pro delivers perfectly usable performance for interactive chat and coding assistance. Check the M4 Pro 24 GB compatibility page and the RTX 4090 compatibility page for side-by-side numbers.
One more thing: if you're on a Mac with 16 GB, I won't pretend the 26B-A4B is a good experience. Stick with the E4B. It's genuinely impressive for 8 billion parameters, and it runs beautifully on 16 GB.
