10 GB VRAM (+2)380 GB/s (+156)
C
Raises estimated decode speed by about 87%.48.1 tok/s decode
Raises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$219 MSRP
Can starcoder2 7b run on Intel Arc A550M 8GB?
YES — Tight Fit
C49Usable
○Estimated from fit model
starcoder2 7b needs ~6.8 GB VRAM. Intel Arc A550M 8GB has 8.0 GB. With Q4_K_M quantization, expect ~26 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 6.8 GB, 25.7 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
25.7 tok/s
TTFT
7532 ms
Safe context
40K
Memory
6.8 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsstarcoder2 7b on Intel Arc A550M 8GB
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: 25.7 tok/s decode · 7.5s TTFT (warm) · 64 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 | 25.7 tok/s | 4108 ms | 40K |
| Coding | C | Tight fit | 25.7 tok/s | 7532 ms | 40K |
| Agentic Coding | C | Runs with offload | 25.7 tok/s | 10955 ms | 40K |
| Reasoning | C | Tight fit | 25.7 tok/s | 8901 ms | 40K |
| RAG | C | Runs with offload | 25.7 tok/s | 13694 ms | 40K |
Quantization options
How starcoder2 7b (7B params) fits at each quantization level on Intel Arc A550M 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 |
Get started
Copy-paste commands to run starcoder2 7b on your machine.
Run
lms load hf-quantfactory--starcoder2-7b-gguf && lms server startUpgrade options
Hardware that runs starcoder2 7b well
12 GB VRAM (+4)456 GB/s (+232)
C
Raises estimated decode speed by about 100%.51.3 tok/s decode
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
16 GB VRAM (+8)560 GB/s (+336)
C
Raises estimated decode speed by about 130%.59 tok/s decode
Raises estimated decode speed by about 130%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP