RX 7900 XT 20GBGünstige Wahl
800 GB/s (+527)
C
Raises estimated decode speed by about 148%.98.4 tok/s Dekodierung
Raises estimated decode speed by about 148%.
ca. $899 MSRP
Can granite 8b code instruct 4k run on MacBook Pro M4 Pro 24GB?
YES — Runs Great
C51Usable
Estimated — low-sample bucket· few comparable runs
granite 8b code instruct 4k needs ~9.3 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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) — 9.3 GB, 39.6 tok/s, Runs well
9.3 GB required17.3 GB available
54% VRAM used
Fit status
Runs well
Decode
39.6 tok/s
TTFT
4885 ms
Safe context
152K
Memory
9.3 GB / 17.3 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom2.6 GB
See how fast it feels
See how fast it feelsgranite 8b code instruct 4k on MacBook Pro M4 Pro 24GB
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: 39.6 tok/s decode · 4.9s TTFT (warm) · 99 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 | 39.6 tok/s | 2665 ms | 152K |
| Coding | C | Runs well | 39.6 tok/s | 4885 ms | 152K |
| Agentic Coding | C | Runs well | 39.6 tok/s | 7106 ms | 152K |
| Reasoning | C | Runs well | 39.6 tok/s | 5773 ms | 152K |
| RAG | C | Runs well | 39.6 tok/s | 8882 ms | 152K |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C46 |
Q3_K_S | 3 | 3.9 GB | Low | C47 |
NVFP4 | 4 | 4.5 GB | Medium | C48 |
Q4_K_M | 4 | 4.9 GB | Medium | C48 |
Q5_K_M | 5 | 5.8 GB | High | C49 |
Q6_K | 6 | 6.6 GB | High | C49 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C51 |
F16 | 16 | 16.4 GB | Maximum | F0 |
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-Optionen
Hardware, die granite 8b code instruct 4k gut ausführt
Frequently asked questions
Can MacBook Pro M4 Pro 24GB run granite 8b code instruct 4k?
Yes, MacBook Pro M4 Pro 24GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 39.6 tok/s.
How much VRAM does granite 8b code instruct 4k need?
granite 8b code instruct 4k (8B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.
What is the best quantization for granite 8b code instruct 4k?
The recommended quantization for granite 8b code instruct 4k is Q4_K_M, which balances quality and memory efficiency.
What speed will granite 8b code instruct 4k run at on MacBook Pro M4 Pro 24GB?
On MacBook Pro M4 Pro 24GB, granite 8b code instruct 4k achieves approximately 39.6 tokens per second decode speed with a time-to-first-token of 4885ms using Q4_K_M quantization.
Can MacBook Pro M4 Pro 24GB run granite 8b code instruct 4k for coding?
For coding workloads, granite 8b code instruct 4k on MacBook Pro M4 Pro 24GB receives a C grade with 39.6 tok/s and 152K context.
What context window can granite 8b code instruct 4k use on MacBook Pro M4 Pro 24GB?
On MacBook Pro M4 Pro 24GB, granite 8b code instruct 4k can safely use up to 152K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 Pro 24GB as fast as VRAM for granite 8b code instruct 4k?
Not always. MacBook Pro M4 Pro 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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