Intel Arc B580 12GBOpción económica12 GB VRAM (+2)456 GB/s (+76)
B
La memoria puede parecer suficiente, pero el ecosistema de software sigue siendo una limitación aquí.55.4 tok/s decodificación
~$249 MSRP
Can Granite 3.1 8B run on Intel Arc B570 10GB?
YES — Tight Fit
B57Good
Estimated from fit model
Granite 3.1 8B needs ~8.7 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 8.7 GB, 52.0 tok/s, Tight fit
8.7 GB required10.0 GB available
87% VRAM used
Fit status
Tight fit
Decode
52.0 tok/s
TTFT
3724 ms
Safe context
26K
Memory
8.7 GB / 10.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsGranite 3.1 8B on Intel Arc B570 10GB
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: 52.0 tok/s decode · 3.7s TTFT (warm) · 130 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 | 52.0 tok/s | 2031 ms | 26K |
| Coding | B | Tight fit | 52.0 tok/s | 3724 ms | 26K |
| Agentic Coding | C | Runs with offload (needs ~0.3 GB host RAM) | 34.7 tok/s | 8111 ms | 26K |
| Reasoning | B | Tight fit | 52.0 tok/s | 4401 ms | 26K |
| RAG | C | Runs with offload (needs ~0.3 GB host RAM) | 34.7 tok/s | 10139 ms | 26K |
Quantization options
How Granite 3.1 8B (8B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B56 |
Q3_K_S | 3 | 3.9 GB | Low | B57 |
NVFP4 | 4 | 4.5 GB | Medium | B58 |
Q4_K_M | 4 | 4.9 GB | Medium | B57 |
Q5_K_M | 5 | 5.8 GB | High | B57 |
Q6_KBest for your GPU | 6 | 6.6 GB | High | B57 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run Granite 3.1 8B on your machine.
Run
ollama run granite3.1-denseOpciones de mejora
Hardware que ejecuta bien Granite 3.1 8B
Intel Arc B580 12GBOpción económicaIntel Arc A770 16GBMejor relación calidad-precio16 GB VRAM (+6)560 GB/s (+180)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.63.8 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$349 MSRP
Intel Arc Pro A60 12GBMejora Intel12 GB VRAM (+2)384 GB/s (+4)
B
La memoria puede parecer suficiente, pero el ecosistema de software sigue siendo una limitación aquí.47.7 tok/s decodificación
~$499 MSRP
Frequently asked questions
Can Intel Arc B570 10GB run Granite 3.1 8B?
Yes, Intel Arc B570 10GB can run Granite 3.1 8B with a B grade (Tight fit). Expected decode speed: 52.0 tok/s.
How much VRAM does Granite 3.1 8B need?
Granite 3.1 8B (8B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.
What is the best quantization for Granite 3.1 8B?
The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will Granite 3.1 8B run at on Intel Arc B570 10GB?
On Intel Arc B570 10GB, Granite 3.1 8B achieves approximately 52.0 tokens per second decode speed with a time-to-first-token of 3724ms using Q4_K_M quantization.
Can Intel Arc B570 10GB run Granite 3.1 8B for coding?
For coding workloads, Granite 3.1 8B on Intel Arc B570 10GB receives a B grade with 52.0 tok/s and 26K context.
What context window can Granite 3.1 8B use on Intel Arc B570 10GB?
On Intel Arc B570 10GB, Granite 3.1 8B can safely use up to 26K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
What should I upgrade first if Granite 3.1 8B feels slow on Intel Arc B570 10GB?
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 B570 10GB for Granite 3.1 8B?
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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