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

YES — Runs Great

B67Good

Estimated from fit model

Yi Coder 9B needs ~9.4 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~49 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) — 9.4 GB, 48.6 tok/s, Runs well

9.4 GB required12.0 GB available

78% VRAM used

Fit status

Runs well

Decode

48.6 tok/s

TTFT

3985 ms

Safe context

45K

Memory

9.4 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi Coder 9B 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: 48.6 tok/s decode · 4.0s TTFT (warm) · 122 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	48.6 tok/s	2173 ms	45K
Coding	B	Runs well	48.6 tok/s	3985 ms	45K
Agentic Coding	B	Tight fit	48.6 tok/s	5796 ms	45K
Reasoning	B	Runs well	48.6 tok/s	4709 ms	45K
RAG	B	Tight fit	48.6 tok/s	7245 ms	45K

Quantization options

How Yi Coder 9B (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	B62
Q3_K_S	3	4.4 GB	Low	B63
NVFP4	4	5.0 GB	Medium	B64
Q4_K_M	4	5.5 GB	Medium	B65
Q5_K_M	5	6.5 GB	High	B64
Q6_KBest for your GPU	6	7.4 GB	High	B64
Q8_0	8	9.6 GB	Very High	F0
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

Upgrade options

Hardware that runs Yi Coder 9B well

RTX 5070 Ti 16GBBudget pick

16 GB VRAM (+4)896 GB/s (+560)

Raises estimated decode speed by about 134%.113.6 tok/s decode

Raises estimated decode speed by about 134%.

Adds memory headroom for longer context windows and future model growth.

~$749 MSRP

RTX 4070 Ti Super 16GBBest value

16 GB VRAM (+4)672 GB/s (+336)

Raises estimated decode speed by about 119%.106.5 tok/s decode

Raises estimated decode speed by about 119%.

Adds memory headroom for longer context windows and future model growth.

~$799 MSRP

RTX 4080 Super 16GBNVIDIA upgrade

16 GB VRAM (+4)736 GB/s (+400)

Raises estimated decode speed by about 149%.121 tok/s decode

Raises estimated decode speed by about 149%.

Adds memory headroom for longer context windows and future model growth.

~$999 MSRP

Frequently asked questions

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

Yes, RTX 3500 Ada Laptop 12GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 48.6 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 9.4 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 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Yi Coder 9B achieves approximately 48.6 tokens per second decode speed with a time-to-first-token of 3985ms using Q4_K_M quantization.

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

For coding workloads, Yi Coder 9B on RTX 3500 Ada Laptop 12GB receives a B grade with 48.6 tok/s and 45K context.

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

On RTX 3500 Ada Laptop 12GB, Yi Coder 9B can safely use up to 45K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 3500 Ada Laptop 12GB See all hardware for Yi Coder 9B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

48.6 tok/s

2173 ms

45K

Coding

Runs well

48.6 tok/s

3985 ms

45K

Agentic Coding

Tight fit

48.6 tok/s

5796 ms

45K

Reasoning

Runs well

48.6 tok/s

4709 ms

45K

RAG

Tight fit

48.6 tok/s

7245 ms

45K

Quantization options

How Yi Coder 9B (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	B62
Q3_K_S	3	4.4 GB	Low	B63
NVFP4	4	5.0 GB	Medium	B64
Q4_K_M	4	5.5 GB	Medium	B65
Q5_K_M	5	6.5 GB	High	B64
Q6_KBest for your GPU	6	7.4 GB	High	B64
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Frequently asked questions

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

Yes, RTX 3500 Ada Laptop 12GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 48.6 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 9.4 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 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Yi Coder 9B achieves approximately 48.6 tokens per second decode speed with a time-to-first-token of 3985ms using Q4_K_M quantization.

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

For coding workloads, Yi Coder 9B on RTX 3500 Ada Laptop 12GB receives a B grade with 48.6 tok/s and 45K context.

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

On RTX 3500 Ada Laptop 12GB, Yi Coder 9B can safely use up to 45K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.