Yes — Gemma 3 4B (4B) runs at 95 tok/s on M4 Pro with 24 GB RAM using Q8_0 quantization via MLX. First token latency is 0.3s. Google's previous-gen 4B dense model, solid balance of speed and quality.
Want to run Gemma 3 4B faster — or step up to bigger models? Find your Mac →The LLMCheck index estimates Gemma 3 4B on M4 Pro using our published methodology: Q4_K_M quantization, memory-bandwidth scaling, and cross-referenced third-party benchmarks where available. Figures are transparent estimates — own this config? Submit a real benchmark →
| Metric | Value |
|---|---|
| Tokens per second | 95 tok/s |
| Time to first token | 0.3s |
| Quantization | Q8_0 |
| Minimum RAM | 24 GB |
| Recommended engine | MLX |
| Parameters | 4B |
| Benchmark date | 2026-02 |
Q8_0 4B MLX M4 Pro
The recommended engine for Gemma 3 4B on M4 Pro is MLX. Install with pip and pull the model:
Alternatively, you can use Ollama for a simpler setup:
| Chip | Speed | First Token | Min RAM | Engine |
|---|---|---|---|---|
| M5 Max | 132 tok/s | 0.3s | 64 GB | Ollama |
| M3 Pro | 88 tok/s | 0.4s | 18 GB | MLX |
| M1 | 48 tok/s | 0.8s | 8 GB | Ollama |
To run Gemma 3 4B on M4 Pro you need:
Gemma 3 4B needs about 24 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
As an Amazon Associate, LLMCheck earns from qualifying purchases. These affiliate links cost you nothing extra and help keep our benchmarks free.
See how Gemma 3 4B stacks up against other models on your specific Mac hardware.
Open Compare Tool Full Leaderboard