Question 1

How to fix OOM errors when running Gemma 4 locally?

Accepted Answer

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.

Question 2

Can I run Gemma 4 on my phone?

Accepted Answer

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.

Question 3

How to run Gemma 4 on LM Studio?

Accepted Answer

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.

Question 4

What's the difference between Gemma 4 26B MoE and 31B Dense?

Accepted Answer

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+).

Question 5

Does Gemma 4 support EXL2 format?

Accepted Answer

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.

Question 6

Can an RTX 4090 run Gemma 4 31B?

Accepted Answer

Yes. The RTX 4090 (24 GB VRAM) can run Gemma 4 31B Dense at Q4_K_M quantization (~20 GB VRAM). You'll get approximately 15–25 t/s depending on context length. For best results, use -np 1 with Ollama to limit parallelism. Long contexts (10k+ tokens) may be tight — reduce with --ctx-size if needed.

Question 7

Can an RTX 4060 run Gemma 4?

Accepted Answer

Yes, an RTX 4060 (8 GB VRAM) can run the E4B model comfortably at Q4_K_M quantization with 20–35 t/s. The 26B MoE model requires 12 GB+ VRAM at minimum, so it won't fit on an RTX 4060. Use Ollama or LM Studio and search for gemma4:e4b to get started.

Question 8

What are the minimum PC requirements for Gemma 4?

Accepted Answer

For the smallest model (E2B), you need a PC with at least 4 GB of free RAM and any modern GPU or CPU. For the popular 26B MoE model, aim for 16 GB of RAM and 12–16 GB of VRAM (RTX 4070 Ti or equivalent). For the full 31B Dense model, you'll want 24 GB+ VRAM (RTX 4090) or 32 GB+ unified memory (Apple Silicon Max chips). All models can run on CPU alone at reduced speed (3–8 t/s).

Question 9

How does the automatic GPU detection work?

Accepted Answer

This tool uses browser-native WebGPU and WebGL APIs to identify your GPU — no downloads required. The detected GPU name is matched against a known VRAM database to auto-fill your hardware profile. Everything runs entirely in your browser.

Question 10

Why does the tool detect my GPU but not my RAM?

Accepted Answer

Browser security restrictions prevent reading exact system RAM. The navigator.deviceMemory API caps at 8 GB and only works in Chromium. GPU names are exposed through WebGPU/WebGL for rendering, so we can detect GPU but need manual RAM selection.

Question 11

The detection shows the wrong GPU or VRAM — what should I do?

Accepted Answer

Click 'Switch to manual selection' below the detected hardware card. Common reasons for inaccurate detection include laptops with dual GPUs, privacy browsers blocking GPU fingerprinting, or newer GPUs not yet in the lookup database.

Gemma 4 Local Hardware MatcherSize uygun olan Gemma 4 model varyantını yerel cihaz donanım ayarınızı için bulun.

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Genel Bakış

Temel özellikler

Fiyatlar

Kullanım senaryoları

GPU'nuzu seçin

Yalnızca CPU'a dayalı inferans ayarları planlayın

Yerel LLM'lerin donanım güncellemelerini değerlendirin

Model kalitesi ve hız dengelenin

Artılar ve eksiler

Artılar

Eksiler

Savaş rekoru

Last 2 battles

İncelemeler

Use it every day

Does the job

Skeptical, then convinced

Solid for our team

Solid for our team

Solid for our team

Sorular

Soru sor

LLM alternatifleri

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