Mac Studio M3 Ultra 256GBOpção econômica
256 GB Unified (+128)
A
Adds memory headroom for longer context windows and future model growth.112 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Can Granite 4.1 8B run on Intel Data Center GPU Max 1550 128GB?
YES — Runs Great
B70Good
Estimated from fit model
Granite 4.1 8B needs ~21.0 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~112 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 21.0 GB, 112.0 tok/s, Runs well
21.0 GB required128.0 GB available
16% VRAM used
Fit status
Runs well
Decode
112.0 tok/s
TTFT
1729 ms
Safe context
131K
Memory
21.0 GB / 128.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsGranite 4.1 8B on Intel Data Center GPU Max 1550 128GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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 | B | Runs well | 112.0 tok/s | 943 ms | 131K |
| Coding | B | Runs well | 112.0 tok/s | 1729 ms | 131K |
| Agentic Coding | A | Runs well | 112.0 tok/s | 2514 ms | 131K |
| Reasoning | B | Runs well | 112.0 tok/s | 2043 ms | 131K |
| RAG | A | Runs well | 112.0 tok/s | 3143 ms | 131K |
Quantization options
How Granite 4.1 8B (8B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B62 |
Q3_K_S | 3 | 3.9 GB | Low | B62 |
NVFP4 | 4 | 4.5 GB | Medium | B62 |
Q4_K_M | 4 | 4.9 GB | Medium | B62 |
Q5_K_M | 5 | 5.8 GB | High | B62 |
Q6_K | 6 | 6.6 GB | High | B62 |
Q8_0 | 8 | 8.6 GB | Very High | B62 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | B63 |
Get started
Copy-paste commands to run Granite 4.1 8B on your machine.
Run
ollama run granite4.1:8bOpções de upgrade
Hardware que roda bem Granite 4.1 8B
Frequently asked questions
Can Intel Data Center GPU Max 1550 128GB run Granite 4.1 8B?
Yes, Intel Data Center GPU Max 1550 128GB can run Granite 4.1 8B with a B grade (Runs well). Expected decode speed: 112.0 tok/s.
How much VRAM does Granite 4.1 8B need?
Granite 4.1 8B (8B parameters) requires approximately 21.0 GB of memory with Q4_K_M quantization.
What is the best quantization for Granite 4.1 8B?
The recommended quantization for Granite 4.1 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will Granite 4.1 8B run at on Intel Data Center GPU Max 1550 128GB?
On Intel Data Center GPU Max 1550 128GB, Granite 4.1 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.
Can Intel Data Center GPU Max 1550 128GB run Granite 4.1 8B for coding?
For coding workloads, Granite 4.1 8B on Intel Data Center GPU Max 1550 128GB receives a B grade with 112.0 tok/s and 131K context.
What context window can Granite 4.1 8B use on Intel Data Center GPU Max 1550 128GB?
On Intel Data Center GPU Max 1550 128GB, Granite 4.1 8B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
What should I upgrade first if Granite 4.1 8B feels slow on Intel Data Center GPU Max 1550 128GB?
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 Data Center GPU Max 1550 128GB for Granite 4.1 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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