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

YES — Runs Great

C52Usable

Estimated from fit model

Yi Coder 9B Chat needs ~9.7 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~91 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) — 9.7 GB, 90.9 tok/s, Runs well

9.7 GB required20.0 GB available

49% VRAM used

Fit status

Runs well

Decode

90.9 tok/s

TTFT

2129 ms

Safe context

172K

Memory

9.7 GB / 20.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat 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: 90.9 tok/s decode · 2.1s TTFT (warm) · 227 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	C	Runs well	90.9 tok/s	1161 ms	172K
Coding	C	Runs well	90.9 tok/s	2129 ms	172K
Agentic Coding	C	Runs well	90.9 tok/s	3097 ms	172K
Reasoning	C	Runs well	90.9 tok/s	2516 ms	172K
RAG	C	Runs well	90.9 tok/s	3871 ms	172K

Quantization options

How Yi Coder 9B Chat (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	C46
Q3_K_S	3	4.4 GB	Low	C46
NVFP4	4	5.0 GB	Medium	C47
Q4_K_M	4	5.5 GB	Medium	C47
Q5_K_M	5	6.5 GB	High	C48
Q6_K	6	7.4 GB	High	C49
Q8_0Best for your GPU	8	9.6 GB	Very High	C50
F16	16	18.5 GB	Maximum	F0

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 start

Frequently asked questions

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

Yes, RTX A4500 20GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 90.9 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 9B Chat?

The recommended quantization for Yi Coder 9B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 9B Chat run at on RTX A4500 20GB?

On RTX A4500 20GB, Yi Coder 9B Chat achieves approximately 90.9 tokens per second decode speed with a time-to-first-token of 2129ms using Q4_K_M quantization.

Can RTX A4500 20GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on RTX A4500 20GB receives a C grade with 90.9 tok/s and 172K context.

What context window can Yi Coder 9B Chat use on RTX A4500 20GB?

On RTX A4500 20GB, Yi Coder 9B Chat can safely use up to 172K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

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

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-coder-9b-chat-gguf-on-rtx-a4500-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: