MacBook Pro M3 Pro 18GBGünstige Wahl
18 GB Unified (+2)
B
This setup is broadly balanced for this model.27.7 tok/s Dekodierung
ca. $1,999 MSRP
Can Granite 3.1 8B run on MacBook Pro M2 Pro 16GB?
YES — Tight Fit
B55Good
Estimated from fit model
Granite 3.1 8B needs ~9.5 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~36 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 9.5 GB, 35.5 tok/s, Tight fit
9.5 GB required11.5 GB available
83% VRAM used
Fit status
Tight fit
Decode
35.5 tok/s
TTFT
5459 ms
Safe context
33K
Memory
9.5 GB / 11.5 GB
Memory breakdown
Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom1.7 GB
See how fast it feels
See how fast it feelsGranite 3.1 8B on MacBook Pro M2 Pro 16GB
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: 35.5 tok/s decode · 5.5s TTFT (warm) · 89 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 | B | Runs well | 35.5 tok/s | 2977 ms | 33K |
| Coding | B | Tight fit | 35.5 tok/s | 5459 ms | 33K |
| Agentic Coding | B | Runs with offload | 35.5 tok/s | 7940 ms | 33K |
| Reasoning | B | Tight fit | 35.5 tok/s | 6451 ms | 33K |
| RAG | B | Runs with offload | 35.5 tok/s | 9925 ms | 33K |
Quantization options
How Granite 3.1 8B (8B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | B56 |
NVFP4 | 4 | 4.5 GB | Medium | B56 |
Q4_K_M | 4 | 4.9 GB | Medium | B57 |
Q5_K_M | 5 | 5.8 GB | High | B57 |
Q6_KBest for your GPU | 6 | 6.6 GB | High | B57 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run Granite 3.1 8B on your machine.
Run
ollama run granite3.1-denseUpgrade-Optionen
Hardware, die Granite 3.1 8B gut ausführt
MacBook Pro M4 Pro 24GBBestes Preis-Leistungs-Verhältnis
24 GB Unified (+8)273 GB/s (+73)
B
Raises estimated decode speed by about 50%.53.3 tok/s Dekodierung
Raises estimated decode speed by about 50%.
Adds memory headroom for longer context windows and future model growth.
ca. $1,999 MSRP
MacBook Pro M2 Max 32GBApple-Upgrade
32 GB Unified (+16)400 GB/s (+200)
B
Raises estimated decode speed by about 66%.58.8 tok/s Dekodierung
Raises estimated decode speed by about 66%.
Adds memory headroom for longer context windows and future model growth.
ca. $1,999 MSRP
Frequently asked questions
Can MacBook Pro M2 Pro 16GB run Granite 3.1 8B?
Yes, MacBook Pro M2 Pro 16GB can run Granite 3.1 8B with a B grade (Tight fit). Expected decode speed: 35.5 tok/s.
How much VRAM does Granite 3.1 8B need?
Granite 3.1 8B (8B parameters) requires approximately 9.5 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 MacBook Pro M2 Pro 16GB?
On MacBook Pro M2 Pro 16GB, Granite 3.1 8B achieves approximately 35.5 tokens per second decode speed with a time-to-first-token of 5459ms using Q4_K_M quantization.
Can MacBook Pro M2 Pro 16GB run Granite 3.1 8B for coding?
For coding workloads, Granite 3.1 8B on MacBook Pro M2 Pro 16GB receives a B grade with 35.5 tok/s and 33K context.
What context window can Granite 3.1 8B use on MacBook Pro M2 Pro 16GB?
On MacBook Pro M2 Pro 16GB, Granite 3.1 8B can safely use up to 33K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Granite 3.1 8B?
Not always. MacBook Pro M2 Pro 16GB 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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<iframe src="https://willitrunai.com/embed/granite-3.1-8b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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