RX 7900 XTX 24GBMelhor custo-benefício
960 GB/s (+560)
C
Raises estimated decode speed by about 148%.112 tok/s decodificação
Raises estimated decode speed by about 148%.
~$999 MSRP
Can granite 8b code instruct 4k run on MacBook Pro M1 Max 32GB?
YES — Runs Great
C49Usable
Estimated from fit model
granite 8b code instruct 4k needs ~10.2 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~45 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) — 10.2 GB, 45.1 tok/s, Runs well
10.2 GB required23.0 GB available
44% VRAM used
Fit status
Runs well
Decode
45.1 tok/s
TTFT
4294 ms
Safe context
236K
Memory
10.2 GB / 23.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsgranite 8b code instruct 4k on MacBook Pro M1 Max 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: 45.1 tok/s decode · 4.3s TTFT (warm) · 113 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 | 45.1 tok/s | 2342 ms | 236K |
| Coding | C | Runs well | 45.1 tok/s | 4294 ms | 236K |
| Agentic Coding | C | Runs well | 45.1 tok/s | 6246 ms | 236K |
| Reasoning | C | Runs well | 45.1 tok/s | 5075 ms | 236K |
| RAG | C | Runs well | 45.1 tok/s | 7808 ms | 236K |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C45 |
Q3_K_S | 3 | 3.9 GB | Low | C45 |
NVFP4 | 4 | 4.5 GB | Medium | C45 |
Q4_K_M | 4 | 4.9 GB | Medium | C46 |
Q5_K_M | 5 | 5.8 GB | High | C46 |
Q6_K | 6 | 6.6 GB | High | C47 |
Q8_0 | 8 | 8.6 GB | Very High | C48 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C50 |
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 startOpções de upgrade
Hardware que roda bem granite 8b code instruct 4k
Frequently asked questions
Can MacBook Pro M1 Max 32GB run granite 8b code instruct 4k?
Yes, MacBook Pro M1 Max 32GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 45.1 tok/s.
How much VRAM does granite 8b code instruct 4k need?
granite 8b code instruct 4k (8B parameters) requires approximately 10.2 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 M1 Max 32GB?
On MacBook Pro M1 Max 32GB, granite 8b code instruct 4k achieves approximately 45.1 tokens per second decode speed with a time-to-first-token of 4294ms using Q4_K_M quantization.
Can MacBook Pro M1 Max 32GB run granite 8b code instruct 4k for coding?
For coding workloads, granite 8b code instruct 4k on MacBook Pro M1 Max 32GB receives a C grade with 45.1 tok/s and 236K context.
What context window can granite 8b code instruct 4k use on MacBook Pro M1 Max 32GB?
On MacBook Pro M1 Max 32GB, granite 8b code instruct 4k can safely use up to 236K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M1 Max 32GB as fast as VRAM for granite 8b code instruct 4k?
Not always. MacBook Pro M1 Max 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.
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