MacBook Pro M2 Pro 32GBコスパ重視
32 GB Unified (+8)200 GB/s (+80)
C
Raises estimated decode speed by about 72%.56 tok/sデコード
Raises estimated decode speed by about 72%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can gemma 3 4b it run on MacBook Air M4 24GB?
YES — Runs Great
C47Usable
Estimated — low-sample bucket· few comparable runs
gemma 3 4b it needs ~6.4 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~35 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 6.4 GB, 32.6 tok/s, Runs well
6.4 GB required17.3 GB available
37% VRAM used
Fit status
Runs well
Decode
32.6 tok/s
TTFT
5943 ms
Safe context
387K
Memory
6.4 GB / 17.3 GB
Memory breakdown
Weights2.4 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom2.6 GB
See how fast it feels
See how fast it feelsgemma 3 4b it on MacBook Air M4 24GB
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: 32.6 tok/s decode · 5.9s TTFT (warm) · 81 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 | 32.6 tok/s | 3242 ms | 387K |
| Coding | C | Runs well | 35.4 tok/s | 5467 ms | 387K |
| Agentic Coding | C | Runs well | 32.6 tok/s | 8644 ms | 387K |
| Reasoning | C | Runs well | 32.6 tok/s | 7023 ms | 387K |
| RAG | C | Runs well | 32.6 tok/s | 10805 ms | 387K |
Quantization options
How gemma 3 4b it (4B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | C46 |
Q3_K_S | 3 | 2.0 GB | Low | C46 |
NVFP4 | 4 | 2.2 GB | Medium | C46 |
Q4_K_M | 4 | 2.4 GB | Medium | C46 |
Q5_K_M | 5 | 2.9 GB | High | C46 |
Q6_K | 6 | 3.3 GB | High | C47 |
Q8_0 | 8 | 4.3 GB | Very High | C48 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | C51 |
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 startアップグレードオプション
gemma 3 4b itを快適に動かすハードウェア
MacBook Pro M2 Max 32GBコスパ最良
32 GB Unified (+8)400 GB/s (+280)
C
Raises estimated decode speed by about 72%.56 tok/sデコード
Raises estimated decode speed by about 72%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
MacBook Pro M1 Pro 32GBAppleアップグレード
32 GB Unified (+8)200 GB/s (+80)
C
Raises estimated decode speed by about 63%.53.3 tok/sデコード
Raises estimated decode speed by about 63%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Frequently asked questions
Can MacBook Air M4 24GB run gemma 3 4b it?
Yes, MacBook Air M4 24GB can run gemma 3 4b it with a C grade (Runs well). Expected decode speed: 35.4 tok/s.
How much VRAM does gemma 3 4b it need?
gemma 3 4b it (4B parameters) requires approximately 6.4 GB of memory with Q4_K_M quantization.
What is the best quantization for gemma 3 4b it?
The recommended quantization for gemma 3 4b it is Q4_K_M, which balances quality and memory efficiency.
What speed will gemma 3 4b it run at on MacBook Air M4 24GB?
On MacBook Air M4 24GB, gemma 3 4b it achieves approximately 35.4 tokens per second decode speed with a time-to-first-token of 5467ms using Q4_K_M quantization.
Can MacBook Air M4 24GB run gemma 3 4b it for coding?
For coding workloads, gemma 3 4b it on MacBook Air M4 24GB receives a C grade with 35.4 tok/s and 387K context.
What context window can gemma 3 4b it use on MacBook Air M4 24GB?
On MacBook Air M4 24GB, gemma 3 4b it can safely use up to 387K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Air M4 24GB as fast as VRAM for gemma 3 4b it?
Not always. MacBook Air M4 24GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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