Gemma 4 VRAM requirements: which GPU or Mac can actually run E4B, 26B, or 31B?
The shortest useful answer is this: E4B is the safe pick for RTX 3060, RTX 3080, and most 8 to 12 GB cards; 26B starts becoming practical on 16 GB hardware or strong Apple Silicon memory; and 31B is really a 24 GB class model. This page turns that into a direct lookup by GPU, GGUF quantization tier, and Apple Silicon memory size.
Part of the setup path: use this page to decide what fits, then go to the Gemma 4 Ollama setup guide to install and run the right tag.
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Quick answer
If you only need the decision and not the theory: start with gemma4:e4b on 6 to 12 GB hardware, move to gemma4:26b only if you have a real 16 GB path or strong Apple Silicon memory, and treat gemma4:31b as a 24 GB class model. If your hardware is right on the edge, use the smaller model at a better GGUF quantization like Q4_K_M or Q5 instead of forcing the larger one into a cramped setup.
Most common mistake: people compare parameter counts before they compare memory fit. In practice, a smaller Gemma 4 tag that runs cleanly is more useful than a larger one that falls back to RAM offloading and becomes unpleasant to use.
Top GPU lookup: the answers people search for most
This is the snippet-style table for the common searches: Gemma 4 on RTX 3060, Gemma 4 on RTX 3080, Gemma 4 on RTX 4080, Gemma 4 on RTX 4090, and Gemma 4 on Apple Silicon. It is intentionally short so you can make the first decision without scrolling through the whole page.
| Hardware | Best starting tag | Safe GGUF target | What usually happens in practice |
|---|---|---|---|
| RTX 3060 12 GB | gemma4:e4b |
Q4_K_M or Q5 |
E4B is clean. 26B only makes sense as an experiment at very low quantization. |
| RTX 3080 10 GB | gemma4:e4b |
Q4_K_M or Q5 |
Excellent E4B card. 26B is usually not worth the squeeze. |
| RTX 4080 16 GB | gemma4:26b |
Q4_K_M or Q5 |
This is where 26B starts feeling like a normal daily model. |
| RTX 4090 24 GB | gemma4:31b |
Q4_K_M or Q5 |
31B becomes realistic, and high-quality 26B is comfortable. |
| M2 Pro / M3 Pro 18 to 36 GB | gemma4:26b |
Q4_K_M |
Strong Apple Silicon tier for 26B if the rest of the machine is not busy. |
| M3 Max / M4 Max 36 GB+ | gemma4:31b |
Q4_K_M or Q5 |
Large unified memory finally makes the bigger tag feel realistic. |
If you are comparing Q4_K_M vs Q5: use Q5 when the machine still has comfortable headroom, and step down to Q4_K_M when your main goal is fitting the model without RAM offloading.
VRAM by model and quantization
These figures are planning ranges for local use. Real usage will be slightly higher once the runtime, context window, and KV cache are active.
| Model | Q2 | Q4 (default) | Q5 | FP16 | What it means in practice |
|---|---|---|---|---|---|
| E2B | ~0.8 GB | ~1.5 GB | ~1.8 GB | ~4 GB | Useful for CPU-heavy or very small-memory setups. |
| E4B | ~2 GB | ~3.5 GB | ~4.2 GB | ~8 GB | The default local starting point for most users. |
| 26B A4B | ~9 GB | ~14 GB | ~17 GB | ~52 GB | Serious jump in quality, but only realistic on stronger hardware. |
| 31B | ~12 GB | ~20 GB | ~24 GB | ~62 GB | Comfortable local use starts on 24 GB-class GPUs. |
For longer chats or larger prompts, keep another 1 to 4 GB of headroom for the KV cache.
Gemma 4 E4B VRAM requirements
If your real search is "Gemma 4 E4B VRAM requirements", the answer is simple: plan around 3.5 GB for a default local tier and give yourself an 8 GB-class GPU if you want normal day-to-day use. The model itself is small, but the runtime, KV cache, and OS headroom are what make 6 to 8 GB the practical floor.
Gemma 4 26B VRAM requirements
If you are checking whether Gemma 4 26B fits, use 14 GB as the real planning number for a comfortable local setup. That is why 16 GB GPUs and stronger Macs are the first tier where 26B starts making sense as a daily model instead of a one-time experiment.
Gemma 4 31B VRAM requirements
If you want the short answer for Gemma 4 31B VRAM requirements, treat it as a 24 GB-class model. Around 20 GB can load the model at a practical local tier, but the extra headroom is what keeps longer prompts and real sessions from turning the setup into a memory problem.
Why E4B at 3.5 GB still wants an 8 GB-class GPU
The number people quote in screenshots is the model weight size, not the full working footprint. That is why gemma4:e4b can look like a 3.5 GB model and still feel cramped on hardware that only barely clears that number.
| What uses memory | Typical E4B example | Why it matters |
|---|---|---|
| Model weights | ~3.5 GB at Q4 | This is the number most people stop at. |
| KV cache | ~0.5 to 4 GB | Grows with conversation length and long prompts. |
| Runtime + drivers | ~1 to 2 GB | The inference stack and GPU driver need their own room. |
| OS headroom | ~1 GB or more | Without spare room, the system starts fighting the model. |
That is why an 8 GB GPU feels normal for E4B even though the raw weight size is much smaller. The usual complaint is not “it does not load” but “it loaded and then became slow once the prompt got longer.”
What each tier feels like
E4B
E4B is where most readers should start. It loads fast, responds well on mainstream GPUs, and gives you enough quality to judge whether Gemma 4 fits your workflow before you spend time on bigger downloads.
26B A4B
26B A4B is the tier people reach for when E4B is clearly too small. It can be worth it, but only when the machine really supports it. If you only barely fit it, the experience usually stops feeling like an upgrade.
31B
31B is the quality-first local tier. It is not the tag you pick just because it exists. It becomes attractive when you already have 24 GB hardware and prompts that justify the extra memory pressure.
GPU lookup: what fits on common cards
Use this table as the practical filter. “Comfortable” means enough room for everyday use. “Tight” means it may run, but the headroom is poor once your session grows.
| GPU | VRAM | E4B | 26B A4B | 31B |
|---|---|---|---|---|
| GTX 1060 / RX 580 | 6 GB | Works at Q4 | No | No |
| RTX 2060 / RX 5700 | 6 to 8 GB | Comfortable | No | No |
| RTX 3060 Ti / RTX 4060 | 8 GB | Comfortable | No | No |
| RTX 3080 10 GB | 10 GB | Comfortable | Q2 only | No |
| RTX 3060 / RTX 4070 | 12 GB | Comfortable | Q2, usually not worth it | No |
| RTX 4070 Ti Super / RTX 4080 | 16 GB | Comfortable | Q4 fits | Q2 only |
| RTX 3090 / RTX 4090 | 24 GB | Comfortable | Q5 comfortable | Q4 fits |
| RTX 6000 Ada / A6000 | 48 GB | Comfortable | FP16 possible | Q5 comfortable |
Most-asked case: on an RTX 3080 10 GB or 12 GB, E4B is the clean answer. You can technically squeeze harder options, but the quality and speed tradeoff usually makes that a poor decision compared with staying on E4B.
RTX 3080 (10 GB / 12 GB)
The RTX 3080 is one of the most common “can I stretch to a bigger Gemma 4 model?” cards, and the answer is more boring than most people hope. Both the 10 GB and 12 GB variants are excellent E4B cards, but neither is a satisfying 26B A4B card at quality settings you would actually want to keep.
- RTX 3080 10 GB: E4B is clean and fast. 26B A4B only fits at Q2, which is usually not worth the quality hit.
- RTX 3080 12 GB: the extra 2 GB does not change the conclusion enough. It is still better to run E4B well than force 26B A4B into an awkward tier.
- What to do instead: if you want a real 26B A4B experience, the practical next step is a 16 GB or 24 GB card, not “accepting Q2 and hoping for the best.”
RTX 4070 / 4070 Ti
The base RTX 4070 and RTX 4070 Ti sit in the same decision bucket for this page because both are 12 GB cards. They are fast cards, but speed does not solve a memory ceiling. For Gemma 4 specifically, they remain better E4B cards than 26B A4B cards.
- RTX 4070: excellent for E4B, borderline for 26B A4B because 12 GB is still a Q2 story.
- RTX 4070 Ti: faster, but still 12 GB. The extra compute does not erase the memory limit.
- RTX 4070 Ti Super: this is where the story changes, because 16 GB is enough to make 26B A4B at Q4 feel practical.
RTX 3060 / RTX 4090: opposite ends of the search funnel
These two cards show why so many Gemma 4 VRAM searches look similar but need opposite answers. The RTX 3060 12 GB search is usually asking whether 26B is possible without ruining usability. The RTX 4090 search is usually asking whether 31B is finally worth it.
- RTX 3060 12 GB: E4B is the reliable default, and it is where most users should stop. 26B can be forced into a low-quantization experiment, but that is not the same as a comfortable daily setup.
- RTX 4090 24 GB: this is where 31B becomes a real local option instead of a theoretical one. It is also enough room to run 26B at a nicer quality target like
Q5. - Practical summary: one search is about protecting usability on a popular midrange card, and the other is about deciding whether the flagship card should spend its headroom on 31B or on a higher-quality 26B.
Mac unified memory planning
Apple Silicon changes the shape of the problem because RAM and VRAM are one shared pool. That makes Macs better local AI machines than the raw GPU label suggests, but macOS still takes its share before your model gets anything.
| Mac configuration | Usable for AI | Best starting point |
|---|---|---|
| 8 GB | ~3 to 4 GB | E4B at Q4 |
| 16 GB | ~10 to 12 GB | E4B comfortably, 26B A4B only if the rest of the system is quiet |
| 18 to 36 GB | ~12 to 30 GB | 26B A4B becomes realistic |
| 64 GB | ~58 GB | 31B at higher-quality quantization becomes practical |
| 96 to 192 GB | Very high | 31B without compromise |
If you are buying specifically for local Gemma 4 use, the sweet spot is not the smallest Mac that technically loads the model. It is the one that still feels normal while the model is running.
CPU-only: yes, but only for the right model
Some readers are not comparing GPUs at all. They are searching for whether Gemma 4 can run on CPU only. It can, but the answer depends on which tag you mean and whether you care about speed or only about correctness.
| Model | RAM target | CPU-only verdict |
|---|---|---|
| E2B (Q4) | 4 GB+ | Reasonable for quick checks and lightweight prompts. |
| E4B (Q4) | 8 GB+ | Usable for short tasks, but clearly slower than GPU use. |
| 26B A4B (Q4) | 20 GB+ | Possible, but too slow for most day-to-day local use. |
| 31B (Q4) | 28 GB+ | Mostly a patience test. Not a practical default. |
The practical answer for CPU-only searches is usually “use E4B or do not bother.” That is the version of the page most readers are really trying to find.
Quantization rules that matter
Quantization is what turns “interesting on paper” into “usable on a real desk.” The practical lesson is simple: a smaller model at a better quantization usually beats a bigger model that barely fits. If you download GGUF files, the decision people actually make most often is not “FP16 or Q4” but Q4_K_M versus Q5.
| Tier | Quality vs FP16 | Use it when |
|---|---|---|
| FP16 | Reference quality | You have workstation or server-class memory. |
| Q5 | Very close | You want the best local quality/size balance. |
| Q4 | Good default | You want easy local use and broad compatibility. |
| Q3 | Noticeable drop | Only when hardware is very constrained. |
| Q2 | Last resort | Only when the model does not fit otherwise. |
In GGUF naming, Q4_K_M is the “please fit cleanly” option and Q5 is the “I still have room, keep more quality” option. That is the real tradeoff behind many zero-click searches.
Decision rules
| Your situation | Start with | Why |
|---|---|---|
| Any system below 8 GB GPU memory | E4B | It is the only path that still feels practical. |
| 8 to 12 GB GPU | E4B | Best balance of quality, speed, and headroom. |
| 16 GB GPU | 26B A4B | This is where the bigger tier starts to make sense. |
| 24 GB GPU | 31B or high-quality 26B A4B | You finally have enough room to choose on output quality instead of fit alone. |
| Mac with 16 GB unified memory | E4B | Stable starting point without turning the rest of the machine into a bottleneck. |
| Mac with 32 GB or more | 26B A4B | Large enough to feel like a meaningful upgrade. |
FAQ
Gemma 4 31B VRAM requirements: how much VRAM do you actually need?
At the default local quantization, plan around 20 GB for the model itself and more once the session grows. That is why 24 GB GPUs are the real entry point for comfortable use.
Gemma 4 26B A4B VRAM requirements: what is the real minimum?
Roughly 14 GB at the default tier. That makes 16 GB hardware the first genuinely comfortable place to run it without treating every conversation like a memory stress test.
Gemma 4 8 GB VRAM: can you run it comfortably?
Yes, with E4B. That is exactly the kind of machine E4B is meant for. The larger tags move beyond “possible” into “not fun” quickly on 8 GB hardware.
Can RTX 3060 12 GB run Gemma 4 26B?
It can be tested at a very aggressive quantization, but the clean recommendation is still E4B. The usual mistake is turning “it loaded once” into “this is the right daily model.”
Can RTX 4090 run Gemma 4 31B well?
Yes. This is the class of GPU where 31B finally feels like a real local model rather than a hardware flex. It is also enough room to choose between 31B at a practical quantization or 26B at a higher-quality target.
Which GGUF quantization should I pick: Q4_K_M or Q5?
Use Q5 when you already know the card has spare room and you want the nicer quality/size balance. Use Q4_K_M when your first priority is fitting the model cleanly and avoiding RAM offloading.
What happens if I am 1 to 2 GB short?
The runtime can offload layers to RAM, but the user experience usually gets much worse. If you are that close to the edge, step down a model size or use a better-fitting quantization instead.