1792 GB/s (+1642)
C
Raises estimated decode speed by about 69%.76 tok/s decode
Raises estimated decode speed by about 69%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can gemma 3 4b it run on MacBook Pro M3 Pro 36GB?
YES — Runs Great
C46Usable
○Estimated from fit model
gemma 3 4b it needs ~7.7 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~45 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Share:
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 7.7 GB, 44.9 tok/s, Runs well
7.7 GB required25.9 GB available
30% VRAM used
Fit status
Runs well
Decode
44.9 tok/s
TTFT
4314 ms
Safe context
638K
Memory
7.7 GB / 25.9 GB
Memory breakdown
Weights2.4 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom3.9 GB
See how fast it feels
See how fast it feelsgemma 3 4b it on MacBook Pro M3 Pro 36GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 44.9 tok/s decode · 4.3s TTFT (warm) · 112 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 44.9 tok/s | 2353 ms | 638K |
| Coding | C | Runs well | 44.9 tok/s | 4314 ms | 638K |
| Agentic Coding | C | Runs well | 44.9 tok/s | 6275 ms | 638K |
| Reasoning | C | Runs well | 44.9 tok/s | 5098 ms | 638K |
| RAG | C | Runs well | 44.9 tok/s | 7844 ms | 638K |
Quantization options
How gemma 3 4b it (4B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | C44 |
Q3_K_S | 3 | 2.0 GB | Low | C44 |
NVFP4 | 4 | 2.2 GB | Medium | C44 |
Q4_K_M | 4 | 2.4 GB | Medium | C44 |
Q5_K_M | 5 | 2.9 GB | High | C44 |
Q6_K | 6 | 3.3 GB | High | C44 |
Q8_0 | 8 | 4.3 GB | Very High | C45 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | C47 |
Get started
Copy-paste commands to run gemma 3 4b it on your machine.
Run
lms load hf-maziyarpanahi--gemma-3-4b-it-gguf && lms server startUpgrade options