RX 7900 XT 20GBコスパ重視
20 GB VRAM (+4)800 GB/s (+576)
B
Raises estimated decode speed by about 296%.95.1 tok/sデコード
Raises estimated decode speed by about 296%.
Adds memory headroom for longer context windows and future model growth.
〜$899 MSRP
Can Yi Coder 9B run on Intel Arc Pro B50 16GB?
YES — Runs Great
B62Good
Estimated from fit model
Yi Coder 9B needs ~9.5 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~24 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 9.5 GB, 24.0 tok/s, Runs well
9.5 GB required16.0 GB available
59% VRAM used
Fit status
Runs well
Decode
24.0 tok/s
TTFT
8080 ms
Safe context
87K
Memory
9.5 GB / 16.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsYi Coder 9B on Intel Arc Pro B50 16GB
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: 24.0 tok/s decode · 8.1s TTFT (warm) · 60 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 | 24.0 tok/s | 4407 ms | 87K |
| Coding | B | Runs well | 24.0 tok/s | 8080 ms | 87K |
| Agentic Coding | B | Runs well | 24.0 tok/s | 11753 ms | 87K |
| Reasoning | B | Runs well | 24.0 tok/s | 9549 ms | 87K |
| RAG | B | Runs well | 24.0 tok/s | 14691 ms | 87K |
Quantization options
How Yi Coder 9B (9B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B60 |
Q3_K_S | 3 | 4.4 GB | Low | B60 |
NVFP4 | 4 | 5.0 GB | Medium | B61 |
Q4_K_M | 4 | 5.5 GB | Medium | B61 |
Q5_K_M | 5 | 6.5 GB | High | B62 |
Q6_K | 6 | 7.4 GB | High | B63 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | B63 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Yi Coder 9B on your machine.
Run
lms load Yi-Coder-9B-Chat && lms server startアップグレードオプション
Yi Coder 9Bを快適に動かすハードウェア
Frequently asked questions
Can Intel Arc Pro B50 16GB run Yi Coder 9B?
Yes, Intel Arc Pro B50 16GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 24.0 tok/s.
How much VRAM does Yi Coder 9B need?
Yi Coder 9B (9B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi Coder 9B?
The recommended quantization for Yi Coder 9B is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi Coder 9B run at on Intel Arc Pro B50 16GB?
On Intel Arc Pro B50 16GB, Yi Coder 9B achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8080ms using Q4_K_M quantization.
Can Intel Arc Pro B50 16GB run Yi Coder 9B for coding?
For coding workloads, Yi Coder 9B on Intel Arc Pro B50 16GB receives a B grade with 24.0 tok/s and 87K context.
What context window can Yi Coder 9B use on Intel Arc Pro B50 16GB?
On Intel Arc Pro B50 16GB, Yi Coder 9B can safely use up to 87K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
What should I upgrade first if Yi Coder 9B feels slow on Intel Arc Pro B50 16GB?
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 Pro B50 16GB for Yi Coder 9B?
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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