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

Can RTX A4500 20GB run Yi Coder 9B?

Yes, RTX A4500 20GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 98.9 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 10.2 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 A4500 20GB?

On RTX A4500 20GB, Yi Coder 9B achieves approximately 98.9 tokens per second decode speed with a time-to-first-token of 1958ms using Q4_K_M quantization.

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

For coding workloads, Yi Coder 9B on RTX A4500 20GB receives a B grade with 98.9 tok/s and 124K context.

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

On RTX A4500 20GB, Yi Coder 9B can safely use up to 124K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

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

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

98.9 tok/s

1068 ms

124K

Coding

Runs well

98.9 tok/s

1958 ms

124K

Agentic Coding

Runs well

98.9 tok/s

2848 ms

124K

Reasoning

Runs well

98.9 tok/s

2314 ms

124K

RAG

Runs well

98.9 tok/s

3560 ms

124K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

B58

Q3_K_S

4.4 GB

Low

B59

NVFP4

5.0 GB

Medium

B59

Q4_K_M

5.5 GB

Medium

B59

Q5_K_M

6.5 GB

High

B60

Q6_K

7.4 GB

High

B61

Q8_0Best for your GPU

9.6 GB

Very High

B63

F16

18.5 GB

Maximum

Frequently asked questions

Can RTX A4500 20GB run Yi Coder 9B?

Yes, RTX A4500 20GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 98.9 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 10.2 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 A4500 20GB?

On RTX A4500 20GB, Yi Coder 9B achieves approximately 98.9 tokens per second decode speed with a time-to-first-token of 1958ms using Q4_K_M quantization.

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

For coding workloads, Yi Coder 9B on RTX A4500 20GB receives a B grade with 98.9 tok/s and 124K context.

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

On RTX A4500 20GB, Yi Coder 9B can safely use up to 124K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.