Can Yi Coder 9B Chat run on RTX 3500 Ada Laptop 12GB?

YES — Runs Great

C55Usable

Estimated from fit model

Yi Coder 9B Chat needs ~8.9 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 8.9 GB, 44.7 tok/s, Runs well

8.9 GB required12.0 GB available

74% VRAM used

Fit status

Runs well

Decode

44.7 tok/s

TTFT

4333 ms

Safe context

62K

Memory

8.9 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on RTX 3500 Ada Laptop 12GB
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: 44.7 tok/s decode · 4.3s TTFT (warm) · 112 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	44.7 tok/s	2364 ms	62K
Coding	C	Runs well	44.7 tok/s	4333 ms	62K
Agentic Coding	C	Tight fit	44.7 tok/s	6303 ms	62K
Reasoning	C	Runs well	44.7 tok/s	5121 ms	62K
RAG	C	Tight fit	44.7 tok/s	7879 ms	62K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C51
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 start

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run Yi Coder 9B Chat?

Yes, RTX 3500 Ada Laptop 12GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 44.7 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 8.9 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 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Yi Coder 9B Chat achieves approximately 44.7 tokens per second decode speed with a time-to-first-token of 4333ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on RTX 3500 Ada Laptop 12GB receives a C grade with 44.7 tok/s and 62K context.

What context window can Yi Coder 9B Chat use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Yi Coder 9B Chat can safely use up to 62K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3500 Ada Laptop 12GB 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-3500-ada-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: