Intel Arc B580 12GBGünstige Wahl12 GB VRAM (+2)456 GB/s (+76)
C
The raw memory story may look fine, but the software ecosystem is still a constraint here.39.9 tok/s Dekodierung
ca. $249 MSRP
Can Yi 9B Coder i1 run on Intel Arc B570 10GB?
YES — Tight Fit
C51Usable
Estimated from fit model
Yi 9B Coder i1 needs ~8.4 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~37 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) — 8.4 GB, 37.4 tok/s, Tight fit
8.4 GB required10.0 GB available
84% VRAM used
Fit status
Tight fit
Decode
37.4 tok/s
TTFT
5180 ms
Safe context
40K
Memory
8.4 GB / 10.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsYi 9B Coder i1 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: 37.4 tok/s decode · 5.2s TTFT (warm) · 93 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 | 37.4 tok/s | 2825 ms | 40K |
| Coding | C | Tight fit | 37.4 tok/s | 5180 ms | 40K |
| Agentic Coding | C | Tight fit | 37.4 tok/s | 7534 ms | 40K |
| Reasoning | C | Tight fit | 37.4 tok/s | 6121 ms | 40K |
| RAG | C | Tight fit | 37.4 tok/s | 9418 ms | 40K |
Quantization options
How Yi 9B Coder i1 (9B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C52 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4 | 4 | 5.0 GB | Medium | C52 |
Q4_K_M | 4 | 5.5 GB | Medium | C52 |
Q5_K_MBest for your GPU | 5 | 6.5 GB | High | C52 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Yi 9B Coder i1 on your machine.
Run
lms load hf-mradermacher--yi-9b-coder-i1-gguf && lms server startUpgrade-Optionen
Hardware, die Yi 9B Coder i1 gut ausführt
Intel Arc B580 12GBGünstige WahlIntel Arc A770 16GBBestes Preis-Leistungs-Verhältnis16 GB VRAM (+6)560 GB/s (+180)
C
Adds memory headroom for longer context windows and future model growth.45.9 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $349 MSRP
Intel Arc Pro A60 12GBIntel-Upgrade12 GB VRAM (+2)384 GB/s (+4)
C
The raw memory story may look fine, but the software ecosystem is still a constraint here.34.3 tok/s Dekodierung
ca. $499 MSRP
Frequently asked questions
Can Intel Arc B570 10GB run Yi 9B Coder i1?
Yes, Intel Arc B570 10GB can run Yi 9B Coder i1 with a C grade (Tight fit). Expected decode speed: 37.4 tok/s.
How much VRAM does Yi 9B Coder i1 need?
Yi 9B Coder i1 (9B parameters) requires approximately 8.4 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 9B Coder i1?
The recommended quantization for Yi 9B Coder i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 9B Coder i1 run at on Intel Arc B570 10GB?
On Intel Arc B570 10GB, Yi 9B Coder i1 achieves approximately 37.4 tokens per second decode speed with a time-to-first-token of 5180ms using Q4_K_M quantization.
Can Intel Arc B570 10GB run Yi 9B Coder i1 for coding?
For coding workloads, Yi 9B Coder i1 on Intel Arc B570 10GB receives a C grade with 37.4 tok/s and 40K context.
What context window can Yi 9B Coder i1 use on Intel Arc B570 10GB?
On Intel Arc B570 10GB, Yi 9B Coder i1 can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Yi 9B Coder i1 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 Yi 9B Coder i1?
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/hf-mradermacher--yi-9b-coder-i1-gguf-on-arc-b570-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: