Can Yi Coder 9B run on RTX A4500 20GB?

YES — Runs Great

B65Good

○Estimated from fit model

Yi Coder 9B needs ~10.2 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~99 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 10.2 GB, 98.9 tok/s, Runs well

10.2 GB required20.0 GB available

51% VRAM used

Fit status

Runs well

Decode

98.9 tok/s

TTFT

1958 ms

Safe context

124K

Memory

10.2 GB / 20.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsYi Coder 9B on RTX A4500 20GB
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: 98.9 tok/s decode · 2.0s TTFT (warm) · 247 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	98.9 tok/s	1068 ms	124K
Coding	B	Runs well	98.9 tok/s	1958 ms	124K
Agentic Coding	B	Runs well	98.9 tok/s	2848 ms	124K
Reasoning	B	Runs well	98.9 tok/s	2314 ms	124K
RAG	B	Runs well	98.9 tok/s	3560 ms	124K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B58
Q3_K_S	3	4.4 GB	Low	B59
NVFP4	4	5.0 GB	Medium	B59
Q4_K_M	4	5.5 GB	Medium	B59
Q5_K_M	5	6.5 GB	High	B60
Q6_K	6	7.4 GB	High	B61
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

Frequently asked questions

See all results for RTX A4500 20GB See all hardware for Yi Coder 9B

URL: https://willitrunai.com/can-run/yi-coder-9b-on-rtx-a4500-20gb