Can Yi Coder 1.5B Chat run on RTX 3500 Ada Laptop 12GB?

YES — Runs Great

C43Usable

Estimated from fit model

Yi Coder 1.5B Chat needs ~3.5 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~21 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) — 3.5 GB, 21.0 tok/s, Runs well

3.5 GB required12.0 GB available

29% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

791K

Memory

3.5 GB / 12.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B 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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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	21.0 tok/s	5029 ms	695K
Coding	C	Runs well	21.0 tok/s	9219 ms	791K
Agentic Coding	C	Runs well	21.0 tok/s	13410 ms	791K
Reasoning	C	Runs well	21.0 tok/s	10895 ms	791K
RAG	C	Runs well	21.0 tok/s	16762 ms	791K

Quantization options

How Yi Coder 1.5B Chat (1.5B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C47
Q3_K_S	3	0.7 GB	Low	C47
NVFP4	4

Get started

Copy-paste commands to run Yi Coder 1.5B Chat on your machine.

Run

lms load hf-maziyarpanahi--yi-coder-1-5b-chat-gguf && lms server start

Upgrade options

Hardware that runs Yi Coder 1.5B Chat well

MacBook Pro M3 Pro 18GBBudget pick

18 GB Unified (+6)

This setup is broadly balanced for this model.21 tok/s decode

~$1,999 MSRP

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run Yi Coder 1.5B Chat?

Yes, RTX 3500 Ada Laptop 12GB can run Yi Coder 1.5B Chat with a C grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Yi Coder 1.5B Chat need?

Yi Coder 1.5B Chat (1.5B parameters) requires approximately 3.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 1.5B Chat?

The recommended quantization for Yi Coder 1.5B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 1.5B Chat run at on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Yi Coder 1.5B Chat achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run Yi Coder 1.5B Chat for coding?

For coding workloads, Yi Coder 1.5B Chat on RTX 3500 Ada Laptop 12GB receives a C grade with 21.0 tok/s and 791K context.

What context window can Yi Coder 1.5B Chat use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Yi Coder 1.5B Chat can safely use up to 791K 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 1.5B Chat

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