DevPulse → Can I Run It? → Gemma 3 4B

Can I Run

Gemma 3 4B

Google's 4B Gemma with vision support. Strong multilingual performance for its size.

ChatVisionMultilingualCode
4B
Parameters
128K
Context
3.0 GB
RAM (Q4_K_M)

RAM by quantization

Lower quantization = less RAM but lower quality. Q4_K_M is the recommended sweet spot for most users.

FormatBitsRAMQualityVerdict
Q4_K_MREC43.0 GBGoodRuns great
Q6_K63.8 GBExcellentRuns great
Q8_084.9 GBExcellentRuns great
F16168.9 GBLosslessRuns great

Which Mac can run Gemma 3 4B?

Based on the recommended Q4_K_M quantization. You need RAM for both the model and your running apps — DevPulse calculates this for you. No CUDA installation. No driver hell. Just Apple Silicon doing what Jensen charges $30K for.

8 GB
Close apps first
~5 GB for apps
16 GB
Runs well
~13 GB for apps
24 GB
Runs great
~21 GB for apps
32 GB
Runs great
~29 GB for apps
36 GB
Runs great
~33 GB for apps
48 GB
Runs great
~45 GB for apps
64 GB
Runs great
~61 GB for apps
96 GB
Runs great
~93 GB for apps
128 GB
Runs great
~125 GB for apps
192 GB
Runs great
~189 GB for apps

Tips for running Gemma 3 4B

1 Best small model for vision tasks — analyze screenshots, diagrams, and UI mockups

2 128K context makes it useful for RAG and long document analysis

3 Runs smoothly on 8 GB Macs at Q4_K_M alongside normal dev tools

Skip the cloud GPU bill

Why pay $3/hr for an A100 when your Mac has unified memory sitting idle? DevPulse tells you if Gemma 3 4B fits right now — factoring in Chrome, Docker, and everything else hogging your RAM.

Download Free

Model details

Provider
Google
Architecture
dense
License
Gemma
Commercial use
Yes
Min RAM
3 GB
Recommended
5 GB
Released
2025-03
Ollama
gemma3:4b
VRAM estimates from CanIRun.ai. Data sourced from llama.cpp, Ollama, and LM Studio.

Tokens per second on Apple Silicon

How fast will Gemma 3 4B run on each chip?

Apple Silicon inference is bandwidth-bound — every generated token streams the model's active weights through unified memory once. Estimates are for single-batch generation at Q4_K_M (3.0 GB) at ~70% of peak bandwidth (typical llama.cpp / Ollama efficiency). Speculative decoding can lift these another 30-60%.

ChipBandwidthSmallest RAM that fitstok/s (est.)
M168 GB/s8 GB~16 tok/s
M2100 GB/s8 GB~23 tok/s
M3100 GB/s8 GB~23 tok/s
M4120 GB/s16 GB~28 tok/s
M2 Pro200 GB/s16 GB~47 tok/s
M3 Pro150 GB/s18 GB~35 tok/s
M4 Pro273 GB/s24 GB~64 tok/s
M2 Max400 GB/s32 GB~93 tok/s
M3 Max400 GB/s36 GB~93 tok/s
M4 Max546 GB/s36 GB~127 tok/s
M2 Ultra800 GB/s64 GB~187 tok/s
M3 Ultra819 GB/s96 GB~191 tok/s

“Smallest RAM that fits” assumes ~40% headroom for context, OS, and your dev stack. Reclaim VRAM before loading →

Running it offline

Local-AI guides for Gemma 3 4B.

Knowing the model fits is half the problem. The other half is keeping your Mac's unified memory free enough to actually load it, and keeping the load alive across a long session.

CLI for local AI agents →

Pre-flight checks, JSON output, exit codes for scripts. Built for Ollama, llama.cpp, LM Studio, and MLX.

Free VRAM before loading →

The 6-step protocol for clearing unified-memory headroom on Apple Silicon, from cheapest to deepest.

Fix Ollama OOM on Mac →

When Ollama says “out of memory” on a Mac with plenty of RAM. Diagnose and fix in one command.

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Run Gemma 3 4B locally. No GPU required.

While cloud GPU prices keep climbing, your Mac can run Gemma 3 4B for free. DevPulse tells you if it fits alongside your dev tools — before you download 3.0 GB of model weights.

Download for macOS

macOS 14+ · Apple Silicon & Intel · Free during launch