12 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 decode
~$249 MSRP
Can Yi Coder 9B Chat run on Intel Arc B570 10GB?
YES — Tight Fit
C51Usable
○Estimated from fit model
Yi Coder 9B Chat 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
Share:
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 Coder 9B Chat 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 Coder 9B Chat (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 |
Get started
Copy-paste commands to run Yi Coder 9B Chat on your machine.
Run
lms load hf-maziyarpanahi--yi-coder-9b-chat-gguf && lms server startUpgrade options
Hardware that runs Yi Coder 9B Chat well
16 GB VRAM (+6)560 GB/s (+180)
C
Adds memory headroom for longer context windows and future model growth.45.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP
12 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 decode
~$499 MSRP