MacBook Pro M4 Max 36GB经济之选
36 GB Unified (+4)410 GB/s (+290)
C
Raises estimated decode speed by about 199%.65.4 tok/s 解码
Raises estimated decode speed by about 199%.
~$2,499 MSRP
Can Granite 3.1 8B run on Mac mini M4 32GB?
YES — Runs Great
C52Usable
Estimated from fit model
Granite 3.1 8B needs ~11.2 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~22 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) — 11.2 GB, 21.9 tok/s, Runs well
11.2 GB required23.0 GB available
49% VRAM used
Fit status
Runs well
Decode
21.9 tok/s
TTFT
8845 ms
Safe context
113K
Memory
11.2 GB / 23.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsGranite 3.1 8B on Mac mini M4 32GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]
The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
"""Sort a list using the quicksort algorithm.
Args:
arr: List of integers to sort.
Returns:
A new sorted list.
"""
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 21.9 tok/s decode · 8.8s TTFT (warm) · 55 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 | 21.9 tok/s | 4825 ms | 113K |
| Coding | C | Runs well | 21.9 tok/s | 8845 ms | 113K |
| Agentic Coding | C | Runs well | 21.9 tok/s | 12866 ms | 113K |
| Reasoning | C | Runs well | 21.9 tok/s | 10453 ms | 113K |
| RAG | C | Runs well | 21.9 tok/s | 16082 ms | 113K |
Quantization options
How Granite 3.1 8B (8B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C49 |
Q3_K_S | 3 | 3.9 GB | Low | C50 |
NVFP4 | 4 | 4.5 GB | Medium | C50 |
Q4_K_M | 4 | 4.9 GB | Medium | C50 |
Q5_K_M | 5 | 5.8 GB | High | C51 |
Q6_K | 6 | 6.6 GB | High | C51 |
Q8_0 | 8 | 8.6 GB | Very High | C53 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C54 |
Get started
Copy-paste commands to run Granite 3.1 8B on your machine.
Run
ollama run granite3.1-dense升级选项
能流畅运行 Granite 3.1 8B 的硬件
MacBook Pro M4 Max 48GB最佳性价比
48 GB Unified (+16)546 GB/s (+426)
C
Raises estimated decode speed by about 298%.87.1 tok/s 解码
Raises estimated decode speed by about 298%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Mac Studio M2 Ultra 64GBApple 升级
64 GB Unified (+32)800 GB/s (+680)
C
Raises estimated decode speed by about 411%.112 tok/s 解码
Raises estimated decode speed by about 411%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Frequently asked questions
Can Mac mini M4 32GB run Granite 3.1 8B?
Yes, Mac mini M4 32GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 21.9 tok/s.
How much VRAM does Granite 3.1 8B need?
Granite 3.1 8B (8B parameters) requires approximately 11.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Granite 3.1 8B?
The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will Granite 3.1 8B run at on Mac mini M4 32GB?
On Mac mini M4 32GB, Granite 3.1 8B achieves approximately 21.9 tokens per second decode speed with a time-to-first-token of 8845ms using Q4_K_M quantization.
Can Mac mini M4 32GB run Granite 3.1 8B for coding?
For coding workloads, Granite 3.1 8B on Mac mini M4 32GB receives a C grade with 21.9 tok/s and 113K context.
What context window can Granite 3.1 8B use on Mac mini M4 32GB?
On Mac mini M4 32GB, Granite 3.1 8B can safely use up to 113K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Is unified memory on Mac mini M4 32GB as fast as VRAM for Granite 3.1 8B?
Not always. Mac mini 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/granite-3.1-8b-on-m4-mini-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: