MacBook Pro M4 Max 36GBGünstige Wahl
36 GB Unified (+12)
C
This setup is broadly balanced for this model.72 tok/s Dekodierung
ca. $2,499 MSRP
Can StarCoder2 7B run on Intel Arc Pro B60 24GB?
YES — Runs Great
C47Usable
Estimated from fit model
StarCoder2 7B needs ~8.1 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~63 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) — 8.1 GB, 63.0 tok/s, Runs well
8.1 GB required24.0 GB available
34% VRAM used
Fit status
Runs well
Decode
63.0 tok/s
TTFT
3075 ms
Safe context
16K
Memory
8.1 GB / 24.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsStarCoder2 7B 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: 63.0 tok/s decode · 3.1s TTFT (warm) · 157 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 | 63.0 tok/s | 1677 ms | 16K |
| Coding | C | Runs well | 63.0 tok/s | 3075 ms | 16K |
| Agentic Coding | C | Runs well | 63.0 tok/s | 4473 ms | 16K |
| Reasoning | C | Runs well | 63.0 tok/s | 3634 ms | 16K |
| RAG | C | Runs well | 63.0 tok/s | 5591 ms | 16K |
Quantization options
How StarCoder2 7B (7B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C44 |
Q3_K_S | 3 | 3.4 GB | Low | C44 |
NVFP4 | 4 | 3.9 GB | Medium | C44 |
Q4_K_M | 4 | 4.3 GB | Medium | C44 |
Q5_K_M | 5 | 5.0 GB | High | C45 |
Q6_K | 6 | 5.7 GB | High | C45 |
Q8_0 | 8 | 7.5 GB | Very High | C46 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C49 |
Get started
Copy-paste commands to run StarCoder2 7B on your machine.
Run
lms load starcoder2-7b && lms server startUpgrade-Optionen
Hardware, die StarCoder2 7B gut ausführt
Frequently asked questions
Can Intel Arc Pro B60 24GB run StarCoder2 7B?
Yes, Intel Arc Pro B60 24GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 63.0 tok/s.
How much VRAM does StarCoder2 7B need?
StarCoder2 7B (7B parameters) requires approximately 8.1 GB of memory with Q4_K_M quantization.
What is the best quantization for StarCoder2 7B?
The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 7B run at on Intel Arc Pro B60 24GB?
On Intel Arc Pro B60 24GB, StarCoder2 7B achieves approximately 63.0 tokens per second decode speed with a time-to-first-token of 3075ms using Q4_K_M quantization.
Can Intel Arc Pro B60 24GB run StarCoder2 7B for coding?
For coding workloads, StarCoder2 7B on Intel Arc Pro B60 24GB receives a C grade with 63.0 tok/s and 16K context.
What context window can StarCoder2 7B use on Intel Arc Pro B60 24GB?
On Intel Arc Pro B60 24GB, StarCoder2 7B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
What should I upgrade first if StarCoder2 7B 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 StarCoder2 7B?
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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<iframe src="https://willitrunai.com/embed/starcoder2-7b-on-arc-pro-b60-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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