Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)
B
Adds memory headroom for longer context windows and future model growth.71.4 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Can Gemma 4 E2B run on Intel Data Center GPU Max 1550 128GB?
YES — Runs Great
B67Good
○Estimated from fit model
Gemma 4 E2B needs ~17.3 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~71 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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) — 17.3 GB, 71.4 tok/s, Runs well
17.3 GB required128.0 GB available
14% VRAM used
Fit status
Runs well
Decode
71.4 tok/s
TTFT
2711 ms
Safe context
128K
Memory
17.3 GB / 128.0 GB
Memory breakdown
Weights3.1 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsGemma 4 E2B 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: 71.4 tok/s decode · 2.7s TTFT (warm) · 179 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 | 71.4 tok/s | 1479 ms | 128K |
| Coding | B | Runs well | 71.4 tok/s | 2711 ms | 128K |
| Agentic Coding | B | Runs well | 71.4 tok/s | 3944 ms | 128K |
| Reasoning | B | Runs well | 71.4 tok/s | 3204 ms | 128K |
| RAG | B | Runs well | 71.4 tok/s | 4930 ms | 128K |
Quantization options
How Gemma 4 E2B (5.099999904632568B 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 | 2.0 GB | Low | B61 |
Q3_K_S | 3 | 2.5 GB | Low | B61 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Gemma 4 E2B on your machine.
Run
ollama run gemma4:e2bUpgrade options