Can Yi Coder 9B run on RTX 4500 Ada 24GB?

YES — Runs Great

B63Good

Estimated from fit model

Yi Coder 9B needs ~10.6 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~68 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) — 10.6 GB, 67.6 tok/s, Runs well

10.6 GB required24.0 GB available

44% VRAM used

Fit status

Runs well

Decode

67.6 tok/s

TTFT

2864 ms

Safe context

131K

Memory

10.6 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsYi Coder 9B on RTX 4500 Ada 24GB
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: 67.6 tok/s decode · 2.9s TTFT (warm) · 169 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	67.6 tok/s	1562 ms	131K
Coding	B	Runs well	67.6 tok/s	2864 ms	131K
Agentic Coding	B	Runs well	67.6 tok/s	4165 ms	131K
Reasoning	B	Runs well	67.6 tok/s	3384 ms	131K
RAG	B	Runs well	67.6 tok/s	5207 ms	131K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B57
Q3_K_S	3	4.4 GB	Low	B57
NVFP4	4	5.0 GB	Medium	B58
Q4_K_M	4	5.5 GB	Medium	B58
Q5_K_M	5	6.5 GB	High	B58
Q6_K	6	7.4 GB	High	B59
Q8_0	8	9.6 GB	Very High	B60
F16Best for your GPU	16	18.5 GB	Maximum	B62

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

Can RTX 4500 Ada 24GB run Yi Coder 9B?

Yes, RTX 4500 Ada 24GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 67.6 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 10.6 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 RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Yi Coder 9B achieves approximately 67.6 tokens per second decode speed with a time-to-first-token of 2864ms using Q4_K_M quantization.

Can RTX 4500 Ada 24GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on RTX 4500 Ada 24GB receives a B grade with 67.6 tok/s and 131K context.

What context window can Yi Coder 9B use on RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Yi Coder 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4500 Ada 24GB See all hardware for Yi Coder 9B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

67.6 tok/s

1562 ms

131K

Coding

Runs well

67.6 tok/s

2864 ms

131K

Agentic Coding

Runs well

67.6 tok/s

4165 ms

131K

Reasoning

Runs well

67.6 tok/s

3384 ms

131K

RAG

Runs well

67.6 tok/s

5207 ms

131K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B57
Q3_K_S	3	4.4 GB	Low	B57
NVFP4	4	5.0 GB	Medium	B58
Q4_K_M	4	5.5 GB	Medium	B58
Q5_K_M	5	6.5 GB	High	B58
Q6_K	6	7.4 GB	High	B59
Q8_0	8	9.6 GB	Very High	B60
F16Best for your GPU	16	18.5 GB	Maximum	B62

Frequently asked questions

Can RTX 4500 Ada 24GB run Yi Coder 9B?

Yes, RTX 4500 Ada 24GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 67.6 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 10.6 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 RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Yi Coder 9B achieves approximately 67.6 tokens per second decode speed with a time-to-first-token of 2864ms using Q4_K_M quantization.

Can RTX 4500 Ada 24GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on RTX 4500 Ada 24GB receives a B grade with 67.6 tok/s and 131K context.

What context window can Yi Coder 9B use on RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Yi Coder 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.