456 GB/s (+256)
C
Raises estimated decode speed by about 56%.44.9 tok/s decode
Raises estimated decode speed by about 56%.
~$249 MSRP
Can granite 8b code instruct 4k run on MacBook Pro M2 Pro 16GB?
YES — Runs Great
C53Usable
○Estimated from fit model
granite 8b code instruct 4k needs ~8.4 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~29 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) — 8.4 GB, 28.7 tok/s, Runs well
8.4 GB required11.5 GB available
73% VRAM used
Fit status
Runs well
Decode
28.7 tok/s
TTFT
6748 ms
Safe context
68K
Memory
8.4 GB / 11.5 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom1.7 GB
See how fast it feels
See how fast it feelsgranite 8b code instruct 4k 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: 28.7 tok/s decode · 6.7s TTFT (warm) · 72 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 | 28.7 tok/s | 3681 ms | 68K |
| Coding | C | Runs well | 28.7 tok/s | 6748 ms | 68K |
| Agentic Coding | C | Runs well | 28.7 tok/s | 9816 ms | 68K |
| Reasoning | C | Runs well | 28.7 tok/s | 7975 ms | 68K |
| RAG | C | Runs well | 28.7 tok/s | 12270 ms | 68K |
Quantization options
How granite 8b code instruct 4k (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 | C50 |
Q3_K_S | 3 | 3.9 GB | Low | C51 |
NVFP4 | 4 |
Get started
Copy-paste commands to run granite 8b code instruct 4k on your machine.
Run
lms load hf-ibm-granite--granite-8b-code-instruct-4k-gguf && lms server startUpgrade options
