Gemma 4 26B A4B needs ~23.4 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~14 tok/s.
Operating mode
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
0.4 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
13.5 tok/s
TTFT
14294 ms
Safe context
14K
Memory
23.4 GB / 23.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 14.0 tok/s | 7527 ms | 14K |
| Coding | A | Runs with offload (needs ~0.2 GB host RAM) | 13.5 tok/s | 14294 ms | 14K |
| Agentic Coding | A | Very compromised (needs ~2.3 GB host RAM) | 11.0 tok/s | 25641 ms | 14K |
| Reasoning | A | Runs with offload (needs ~0.2 GB host RAM) | 13.5 tok/s | 16893 ms | 14K |
| RAG | A | Very compromised (needs ~2.3 GB host RAM) | 11.0 tok/s |
How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.8 GB | Low | A84 |
Q3_K_S | 3 | 12.3 GB | Low | S85 |
NVFP4 | 4 |
Copy-paste commands to run Gemma 4 26B A4B on your machine.
Run
ollama run gemma4:26bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 11.7 tok/s | |
| Qwen 3.5 27B | 27B | S | 8.6 tok/s |
Yes, MacBook Pro M4 32GB can run Gemma 4 26B A4B with a A grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 13.5 tok/s.
Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 23.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 4 26B A4B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 32GB, Gemma 4 26B A4B achieves approximately 13.5 tokens per second decode speed with a time-to-first-token of 14294ms using Q4_K_M quantization.
For coding workloads, Gemma 4 26B A4B on MacBook Pro M4 32GB receives a A grade with 13.5 tok/s and 14K context.
On MacBook Pro M4 32GB, Gemma 4 26B A4B can safely use up to 14K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-4-26b-a4b-on-m4-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 32051 ms |
| 14K |
14.1 GB |
| Medium |
| S85 |
Q4_K_M | 4 | 15.4 GB | Medium | A85 |
Q5_K_MBest for your GPU | 5 | 18.1 GB | High | A84 |
Q6_K | 6 | 20.7 GB | High | F0 |
Q8_0 | 8 | 27.0 GB | Very High | F0 |
F16 | 16 | 51.7 GB | Maximum | F0 |
| Qwen 3.6 27B | 27B | S | 7.1 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 12.4 tok/s |
| Qwen 3.5 35B A3B | 35B | A | 10.2 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M4 32GB 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.