Intel Arc Pro B60 24GBコスパ重視24 GB VRAM (+8)456 GB/s (+232)
B
Raises estimated decode speed by about 104%.65.2 tok/sデコード
Raises estimated decode speed by about 104%.
Adds memory headroom for longer context windows and future model growth.
〜$599 MSRP
Can Gemma 4 E2B run on Intel Arc Pro B50 16GB?
YES — Runs Great
B70Good
Estimated from fit model
Gemma 4 E2B needs ~6.1 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~32 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 6.1 GB, 32.0 tok/s, Runs well
6.1 GB required16.0 GB available
38% VRAM used
Fit status
Runs well
Decode
32.0 tok/s
TTFT
6042 ms
Safe context
128K
Memory
6.1 GB / 16.0 GB
Memory breakdown
Weights3.1 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsGemma 4 E2B on Intel Arc Pro B50 16GB
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: 32.0 tok/s decode · 6.0s TTFT (warm) · 80 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 | 32.0 tok/s | 3295 ms | 128K |
| Coding | B | Runs well | 32.0 tok/s | 6042 ms | 128K |
| Agentic Coding | A | Runs well | 32.0 tok/s | 8788 ms | 128K |
| Reasoning | B | Runs well | 32.0 tok/s | 7140 ms | 128K |
| RAG | A | Runs well | 32.0 tok/s | 10985 ms | 128K |
Quantization options
How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.0 GB | Low | B69 |
Q3_K_S | 3 | 2.5 GB | Low | B69 |
NVFP4 | 4 | 2.9 GB | Medium | B70 |
Q4_K_M | 4 | 3.1 GB | Medium | B70 |
Q5_K_M | 5 | 3.7 GB | High | A70 |
Q6_K | 6 | 4.2 GB | High | A71 |
Q8_0 | 8 | 5.5 GB | Very High | A72 |
F16Best for your GPU | 16 | 10.5 GB | Maximum | A74 |
Get started
Copy-paste commands to run Gemma 4 E2B on your machine.
Run
ollama run gemma4:e2bアップグレードオプション
Gemma 4 E2Bを快適に動かすハードウェア
Frequently asked questions
Can Intel Arc Pro B50 16GB run Gemma 4 E2B?
Yes, Intel Arc Pro B50 16GB can run Gemma 4 E2B with a B grade (Runs well). Expected decode speed: 32.0 tok/s.
How much VRAM does Gemma 4 E2B need?
Gemma 4 E2B (5.099999904632568B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Gemma 4 E2B?
The recommended quantization for Gemma 4 E2B is Q4_K_M, which balances quality and memory efficiency.
What speed will Gemma 4 E2B run at on Intel Arc Pro B50 16GB?
On Intel Arc Pro B50 16GB, Gemma 4 E2B achieves approximately 32.0 tokens per second decode speed with a time-to-first-token of 6042ms using Q4_K_M quantization.
Can Intel Arc Pro B50 16GB run Gemma 4 E2B for coding?
For coding workloads, Gemma 4 E2B on Intel Arc Pro B50 16GB receives a B grade with 32.0 tok/s and 128K context.
What context window can Gemma 4 E2B use on Intel Arc Pro B50 16GB?
On Intel Arc Pro B50 16GB, Gemma 4 E2B 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 Gemma 4 E2B feels slow on Intel Arc Pro B50 16GB?
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 B50 16GB for Gemma 4 E2B?
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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