Intel Arc Pro B60 24GBOpção econômica24 GB VRAM (+8)
B
Adds memory headroom for longer context windows and future model growth.31.1 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Can Qwen 2.5 Coder 14B run on Intel Arc A770 16GB?
YES — Tight Fit
B65Good
Estimated from fit model
Qwen 2.5 Coder 14B needs ~14.0 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~32 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 14.0 GB, 31.9 tok/s, Tight fit
14.0 GB required16.0 GB available
88% VRAM used
Fit status
Tight fit
Decode
31.9 tok/s
TTFT
6075 ms
Safe context
27K
Memory
14.0 GB / 16.0 GB
Memory breakdown
Weights8.5 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsQwen 2.5 Coder 14B on Intel Arc A770 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: 31.9 tok/s decode · 6.1s 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 | 31.9 tok/s | 3314 ms | 27K |
| Coding | B | Tight fit | 31.9 tok/s | 6075 ms | 27K |
| Agentic Coding | C | Runs with offload (needs ~0.5 GB host RAM) | 21.3 tok/s | 13219 ms | 27K |
| Reasoning | B | Tight fit | 31.9 tok/s | 7179 ms | 27K |
| RAG | C | Runs with offload (needs ~0.5 GB host RAM) | 21.3 tok/s | 16524 ms | 27K |
Quantization options
How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B64 |
Q3_K_S | 3 | 6.9 GB | Low | B65 |
NVFP4 | 4 | 7.8 GB | Medium | B66 |
Q4_K_M | 4 | 8.5 GB | Medium | B66 |
Q5_K_M | 5 | 10.1 GB | High | B65 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | B65 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.
Run
ollama run qwen2.5-coder:14bOpções de upgrade
Hardware que roda bem Qwen 2.5 Coder 14B
Frequently asked questions
Can Intel Arc A770 16GB run Qwen 2.5 Coder 14B?
Yes, Intel Arc A770 16GB can run Qwen 2.5 Coder 14B with a B grade (Tight fit). Expected decode speed: 31.9 tok/s.
How much VRAM does Qwen 2.5 Coder 14B need?
Qwen 2.5 Coder 14B (14B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen 2.5 Coder 14B?
The recommended quantization for Qwen 2.5 Coder 14B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen 2.5 Coder 14B run at on Intel Arc A770 16GB?
On Intel Arc A770 16GB, Qwen 2.5 Coder 14B achieves approximately 31.9 tokens per second decode speed with a time-to-first-token of 6075ms using Q4_K_M quantization.
Can Intel Arc A770 16GB run Qwen 2.5 Coder 14B for coding?
For coding workloads, Qwen 2.5 Coder 14B on Intel Arc A770 16GB receives a B grade with 31.9 tok/s and 27K context.
What context window can Qwen 2.5 Coder 14B use on Intel Arc A770 16GB?
On Intel Arc A770 16GB, Qwen 2.5 Coder 14B can safely use up to 27K 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 14B feels slow on Intel Arc A770 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 A770 16GB for Qwen 2.5 Coder 14B?
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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