MacBook Pro M4 Max 36GB经济之选
36 GB Unified (+12)
C
This setup is broadly balanced for this model.65.4 tok/s 解码
~$2,499 MSRP
Can Granite 3.1 8B run on Intel Arc Pro B60 24GB?
YES — Runs Great
C54Usable
Estimated from fit model
Granite 3.1 8B needs ~10.1 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~62 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 10.1 GB, 62.4 tok/s, Runs well
10.1 GB required24.0 GB available
42% VRAM used
Fit status
Runs well
Decode
62.4 tok/s
TTFT
3104 ms
Safe context
128K
Memory
10.1 GB / 24.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsGranite 3.1 8B on Intel Arc Pro B60 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: 62.4 tok/s decode · 3.1s TTFT (warm) · 156 tok/s prefill
What limits this setup
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Best improvement path
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 62.4 tok/s | 1693 ms | 128K |
| Coding | C | Runs well | 62.4 tok/s | 3104 ms | 128K |
| Agentic Coding | B | Runs well | 62.4 tok/s | 4514 ms | 128K |
| Reasoning | C | Runs well | 62.4 tok/s | 3668 ms | 128K |
| RAG | B | Runs well | 62.4 tok/s | 5643 ms | 128K |
Quantization options
How Granite 3.1 8B (8B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C49 |
Q3_K_S | 3 | 3.9 GB | Low | C49 |
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 | C52 |
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
Frequently asked questions
Can Intel Arc Pro B60 24GB run Granite 3.1 8B?
Yes, Intel Arc Pro B60 24GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 62.4 tok/s.
How much VRAM does Granite 3.1 8B need?
Granite 3.1 8B (8B parameters) requires approximately 10.1 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 Intel Arc Pro B60 24GB?
On Intel Arc Pro B60 24GB, Granite 3.1 8B achieves approximately 62.4 tokens per second decode speed with a time-to-first-token of 3104ms using Q4_K_M quantization.
Can Intel Arc Pro B60 24GB run Granite 3.1 8B for coding?
For coding workloads, Granite 3.1 8B on Intel Arc Pro B60 24GB receives a C grade with 62.4 tok/s and 128K context.
What context window can Granite 3.1 8B use on Intel Arc Pro B60 24GB?
On Intel Arc Pro B60 24GB, Granite 3.1 8B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
What should I upgrade first if Granite 3.1 8B feels slow on Intel Arc Pro B60 24GB?
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Would CUDA be a better path than Intel Arc Pro B60 24GB for Granite 3.1 8B?
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
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