Gemma 4 Local Hardware MatcherFind the right Gemma 4 model variant for your local hardware setup.
Overview
Key features
- Hardware detection and analysis
- Model size and quantization recommendations
- VRAM and RAM requirement estimates
- Performance expectations per variant
- Support for multiple Gemma 4 versions
- Guidance for CPU and GPU inference
Pricing
- Model
- Free
- Category
- LLM
- Rating
- 4.3 / 5 (6)
Use cases
Pick the right Gemma 4 variant for your GPU
Developers can quickly determine which Gemma 4 size and quantization level fits their available VRAM, avoiding out-of-memory crashes during local inference.
Plan CPU-only inference setups
Hobbyists without dedicated GPUs can use the matcher to find a Gemma 4 variant that runs acceptably on system RAM and CPU, with realistic performance expectations.
Evaluate hardware upgrades for local LLMs
Researchers can compare which Gemma 4 versions become accessible at different VRAM or RAM tiers, helping justify hardware investments for local model work.
Balance model quality and speed
Users can review recommended quantization levels to trade off output quality against inference speed, choosing a variant best suited to their workflow.
Pros & Cons
Pros
- Saves time evaluating model compatibility
- Considers quantization options for limited hardware
- Useful for both beginners and advanced users
- Helps avoid out-of-memory failures
Cons
- Limited to the Gemma 4 model family
- Recommendations depend on accurate hardware detection
- May not account for every runtime or backend
Battle record
Across 2 battles in the Pantheon.
Last 2 battles
Reviews
Average from 6 ratings.
Sign in to leave a review.
Use it every day
Honestly didn't expect to like it this much. Support for multiple Gemma 4 versions is exactly what I needed, and useful for both beginners and advanced users. I do wish recommendations depend on accurate hardware detection, but I reach for it almost every day now and it just clicks.
Does the job
Pretty happy overall. Support for multiple Gemma 4 versions just works and useful for both beginners and advanced users. Recommendations depend on accurate hardware detection can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on support for multiple Gemma 4 versions, and helps avoid out-of-memory failures caught me off guard. still, I'd recommend giving it a real trial.
Solid for our team
We rolled this out across the team last quarter and saves time evaluating model compatibility. Model size and quantization recommendations fits neatly into how we already work, and vRAM and RAM requirement estimates removed a step we used to do by hand. Recommendations depend on accurate hardware detection, which is the main caveat, but it has held up under daily use.
Solid for our team
We rolled this out across the team last quarter and useful for both beginners and advanced users. VRAM and RAM requirement estimates fits neatly into how we already work, and vRAM and RAM requirement estimates removed a step we used to do by hand. May not account for every runtime or backend, which is the main caveat, but it has held up under daily use.
Solid for our team
We rolled this out across the team last quarter and useful for both beginners and advanced users. Performance expectations per variant fits neatly into how we already work, and guidance for CPU and GPU inference removed a step we used to do by hand. Limited to the Gemma 4 model family, which is the main caveat, but it has held up under daily use.
Q&A
How to fix OOM errors when running Gemma 4 locally?
Out-of-memory errors mean the model + context window exceeds your available VRAM/RAM. Try these steps in order: 1) Reduce context length — use --ctx-size 4096 or lower. 2) Use a lower quantization — switch from Q4_K_M to Q3 or Q2. 3) Drop to a smaller model — use 26B MoE instead of 31B, or E4B instead of 26B. 4) Limit parallelism — use OLLAMA_NUM_PARALLEL=1 with Ollama. The KV cache for Gemma 4 is particularly large due to its 256K context window, so context length is the single biggest VRAM factor.
Asked by Elif Yildiz · Feb 15, 2026
Can I run Gemma 4 on my phone?
Yes. Install Google AI Edge Gallery on Android or iOS, then download the Gemma 4 E2B model (5B parameters, ~3 GB). It runs entirely offline with no API key. The E4B model (9B parameters) may work on phones with 10+ GB RAM but can crash on devices with less. For best results on mobile, stick to E2B.
Asked by Celia Ramirez · Jan 29, 2026
How to run Gemma 4 on LM Studio?
Open LM Studio, go to the search tab, and type 'gemma 4'. You'll find GGUF files for all model tiers (E2B, E4B, 26B MoE, 31B Dense) in various quantization levels. LM Studio automatically highlights which versions fit your available VRAM. Click download, then switch to the chat tab to start talking. LM Studio also supports EXL2 format for NVIDIA GPUs.
Asked by Yuki Kobayashi · Jan 13, 2026
What's the difference between Gemma 4 26B MoE and 31B Dense?
The 26B-A4B MoE (Mixture of Experts) has 27B total parameters but only activates ~4B per token. This means it's much faster than its size suggests — comparable speed to a 4B model — while retaining higher quality. It fits in 12–16 GB VRAM and is the best choice for consumer hardware (RTX 4070 Ti, MacBook Pro 16 GB). The 31B Dense activates all 31B parameters on every token, giving it the highest quality output but requiring 20+ GB VRAM. It's best for workstations (RTX 4090, M-series 32 GB+).
Asked by Marisol Pena · Jan 13, 2026
Does Gemma 4 support EXL2 format?
Yes. Gemma 4 models (including the 31B Dense) have been converted to EXL2 format by the community. EXL2 files are available on HuggingFace and can be used with ExLlamaV2 for the fastest NVIDIA inference. EXL2 supports flexible bit-rate quantization, letting you fine-tune the VRAM/quality tradeoff. Note: EXL2 requires an NVIDIA GPU with CUDA — it does not support AMD or Apple Silicon.
Asked by Gabriel Duarte · Jan 6, 2026
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