10 GB VRAM (+2)
C
Adds memory headroom for longer context windows and future model growth.48.1 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$219 MSRP
Can StarCoder2 7B run on Intel Arc A750 8GB?
YES — Tight Fit
C52Usable
○Estimated from fit model
StarCoder2 7B needs ~6.8 GB VRAM. Intel Arc A750 8GB has 8.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 6.8 GB, 51.6 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
51.6 tok/s
TTFT
3755 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 A750 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: 51.6 tok/s decode · 3.8s TTFT (warm) · 129 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 | 51.6 tok/s | 2048 ms | 40K |
| Coding | C | Tight fit | 51.6 tok/s | 3755 ms | 40K |
| Agentic Coding | C | Runs with offload | 51.6 tok/s | 5462 ms | 40K |
| Reasoning | C | Tight fit | 51.6 tok/s | 4438 ms | 40K |
| RAG | C | Runs with offload | 51.6 tok/s | 6827 ms | 40K |
Quantization options
How StarCoder2 7B (7B params) fits at each quantization level on Intel Arc A750 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 | 3.9 GB | Medium | C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C53 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run StarCoder2 7B on your machine.
Run
lms load hf-second-state--starcoder2-7b-gguf && lms server startUpgrade options
Hardware that runs StarCoder2 7B well
12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.51.3 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.44.1 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP