RX 7900 XT 20GBコスパ重視
20 GB VRAM (+4)800 GB/s (+576)
A
Raises estimated decode speed by about 218%.98 tok/sデコード
Raises estimated decode speed by about 218%.
Adds memory headroom for longer context windows and future model growth.
〜$899 MSRP
Can Qwen 2.5 Coder 7B run on Intel Arc Pro B50 16GB?
YES — Runs Great
B68Good
Estimated from fit model
Qwen 2.5 Coder 7B needs ~7.6 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~31 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) — 7.6 GB, 30.8 tok/s, Runs well
7.6 GB required16.0 GB available
48% VRAM used
Fit status
Runs well
Decode
30.8 tok/s
TTFT
6295 ms
Safe context
131K
Memory
7.6 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 7B 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: 30.8 tok/s decode · 6.3s TTFT (warm) · 77 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 | 30.8 tok/s | 3433 ms | 131K |
| Coding | B | Runs well | 30.8 tok/s | 6295 ms | 131K |
| Agentic Coding | B | Runs well | 30.8 tok/s | 9156 ms | 131K |
| Reasoning | B | Runs well | 28.3 tok/s | 8077 ms | 131K |
| RAG | B | Runs well | 30.8 tok/s | 11445 ms | 131K |
Quantization options
How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B67 |
Q3_K_S | 3 | 3.4 GB | Low | B67 |
NVFP4 | 4 | 3.9 GB | Medium | B68 |
Q4_K_M | 4 | 4.3 GB | Medium | B68 |
Q5_K_M | 5 | 5.0 GB | High | B69 |
Q6_K | 6 | 5.7 GB | High | B69 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A71 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run Qwen 2.5 Coder 7B on your machine.
Run
ollama run qwen2.5-coder:7bアップグレードオプション
Qwen 2.5 Coder 7Bを快適に動かすハードウェア
Frequently asked questions
Can Intel Arc Pro B50 16GB run Qwen 2.5 Coder 7B?
Yes, Intel Arc Pro B50 16GB can run Qwen 2.5 Coder 7B with a B grade (Runs well). Expected decode speed: 30.8 tok/s.
How much VRAM does Qwen 2.5 Coder 7B need?
Qwen 2.5 Coder 7B (7B parameters) requires approximately 7.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen 2.5 Coder 7B?
The recommended quantization for Qwen 2.5 Coder 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen 2.5 Coder 7B run at on Intel Arc Pro B50 16GB?
On Intel Arc Pro B50 16GB, Qwen 2.5 Coder 7B achieves approximately 30.8 tokens per second decode speed with a time-to-first-token of 6295ms using Q4_K_M quantization.
Can Intel Arc Pro B50 16GB run Qwen 2.5 Coder 7B for coding?
For coding workloads, Qwen 2.5 Coder 7B on Intel Arc Pro B50 16GB receives a B grade with 30.8 tok/s and 131K context.
What context window can Qwen 2.5 Coder 7B use on Intel Arc Pro B50 16GB?
On Intel Arc Pro B50 16GB, Qwen 2.5 Coder 7B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
What should I upgrade first if Qwen 2.5 Coder 7B 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 Qwen 2.5 Coder 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-7b-on-arc-pro-b50-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: