10 GB VRAM (+2)380 GB/s (+156)
C
Raises estimated decode speed by about 165%.37.4 tok/s decode
Raises estimated decode speed by about 165%.
Adds memory headroom for longer context windows and future model growth.
~$219 MSRP
Can Yi 9B Coder i1 run on Intel Arc A550M 8GB?
YES — With Offload
C48Usable
○Estimated from fit model
Yi 9B Coder i1 needs ~8.2 GB VRAM. Intel Arc A550M 8GB has 8.0 GB. With Q4_K_M quantization, expect ~14 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 8.2 GB, 14.1 tok/s, Runs with offload (needs ~0.2 GB host RAM)
8.2 GB required8.0 GB available
102% VRAM needed
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
14.1 tok/s
TTFT
13757 ms
Safe context
12K
Memory
8.2 GB / 8.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsYi 9B Coder i1 on Intel Arc A550M 8GB
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: 14.1 tok/s decode · 13.8s TTFT (warm) · 35 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 | C | Runs with offload | 20.0 tok/s | 5282 ms | 12K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 14.1 tok/s | 13757 ms | 12K |
| Agentic Coding | D | Very compromised (needs ~0.8 GB host RAM) | 10.9 tok/s | 25780 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 14.1 tok/s | 16258 ms | 12K |
| RAG | D | Very compromised (needs ~0.8 GB host RAM) | 10.9 tok/s |
Quantization options
How Yi 9B Coder i1 (9B params) fits at each quantization level on Intel Arc A550M 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C53 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4Best for your GPU |
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 options
Hardware that runs Yi 9B Coder i1 well
12 GB VRAM (+4)456 GB/s (+232)
C
Raises estimated decode speed by about 183%.39.9 tok/s decode
Raises estimated decode speed by about 183%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
16 GB VRAM (+8)560 GB/s (+336)
C
Raises estimated decode speed by about 226%.45.9 tok/s decode
Raises estimated decode speed by about 226%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP