Intel Arc A580 8GBOpção econômica8 GB VRAM (+2)512 GB/s (+326)
A
Adds memory headroom for longer context windows and future model growth.42 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$179 MSRP
Can Granite Code 3B run on Intel Arc A380 6GB?
YES — With Offload
B67Good
Estimated from fit model
Granite Code 3B needs ~5.8 GB VRAM. Intel Arc A380 6GB has 6.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 5.8 GB, 42.0 tok/s, Runs with offload
5.8 GB required6.0 GB available
97% VRAM used
Fit status
Runs with offload
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
8K
Memory
5.8 GB / 6.0 GB
Memory breakdown
Weights1.8 GB
KV Cache2.5 GB
Runtime0.9 GB
Headroom0.6 GB
See how fast it feels
See how fast it feelsGranite Code 3B on Intel Arc A380 6GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 tok/s prefill
What limits this setup
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 42.0 tok/s | 2514 ms | 8K |
| Coding | B | Runs with offload | 42.0 tok/s | 4610 ms | 8K |
| Agentic Coding | F | Too heavy | 19.0 tok/s | 14828 ms | 8K |
| Reasoning | B | Runs with offload | 42.0 tok/s | 5448 ms | 8K |
| RAG | F | Too heavy | 19.0 tok/s | 18535 ms | 8K |
Quantization options
How Granite Code 3B (3B params) fits at each quantization level on Intel Arc A380 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | B69 |
Q3_K_S | 3 | 1.5 GB | Low | A70 |
NVFP4 | 4 | 1.7 GB | Medium | A71 |
Q4_K_M | 4 | 1.8 GB | Medium | A71 |
Q5_K_M | 5 | 2.2 GB | High | A71 |
Q6_K | 6 | 2.5 GB | High | A70 |
Q8_0Best for your GPU | 8 | 3.2 GB | Very High | B70 |
F16 | 16 | 6.1 GB | Maximum | F0 |
Get started
Copy-paste commands to run Granite Code 3B on your machine.
Run
ollama run granite-code:3bOpções de upgrade
Hardware que roda bem Granite Code 3B
Intel Arc A580 8GBOpção econômicaIntel Arc B570 10GBMelhor custo-benefício10 GB VRAM (+4)380 GB/s (+194)
B
Adds memory headroom for longer context windows and future model growth.42 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$219 MSRP
Intel Arc A750 8GBUpgrade Intel8 GB VRAM (+2)512 GB/s (+326)
A
Adds memory headroom for longer context windows and future model growth.42 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$289 MSRP
Frequently asked questions
Can Intel Arc A380 6GB run Granite Code 3B?
Yes, Intel Arc A380 6GB can run Granite Code 3B with a B grade (Runs with offload). Expected decode speed: 42.0 tok/s.
How much VRAM does Granite Code 3B need?
Granite Code 3B (3B parameters) requires approximately 5.8 GB of memory with Q4_K_M quantization.
What is the best quantization for Granite Code 3B?
The recommended quantization for Granite Code 3B is Q4_K_M, which balances quality and memory efficiency.
What speed will Granite Code 3B run at on Intel Arc A380 6GB?
On Intel Arc A380 6GB, Granite Code 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
Can Intel Arc A380 6GB run Granite Code 3B for coding?
For coding workloads, Granite Code 3B on Intel Arc A380 6GB receives a B grade with 42.0 tok/s and 8K context.
What context window can Granite Code 3B use on Intel Arc A380 6GB?
On Intel Arc A380 6GB, Granite Code 3B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
What should I upgrade first if Granite Code 3B feels slow on Intel Arc A380 6GB?
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 A380 6GB for Granite Code 3B?
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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