Can Yi Coder 1.5B run on RTX 4090 Laptop 16GB?

YES — Runs Great

C42Usable

Estimated from fit model

Yi Coder 1.5B needs ~3.6 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 3.6 GB, 24.0 tok/s, Runs well

3.6 GB required16.0 GB available

23% VRAM used

Fit status

Runs well

Decode

24.0 tok/s

TTFT

8067 ms

Safe context

1.1M

Memory

3.6 GB / 16.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B on RTX 4090 Laptop 16GB
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: 24.0 tok/s decode · 8.1s TTFT (warm) · 60 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	24.0 tok/s	4400 ms	1.0M
Coding	C	Runs well	24.0 tok/s	8067 ms	1.1M
Agentic Coding	C	Runs well	24.0 tok/s	11733 ms	1.1M
Reasoning	C	Runs well	24.0 tok/s	9533 ms	1.1M
RAG	C	Runs well	24.0 tok/s	14667 ms	1.1M

Quantization options

How Yi Coder 1.5B (1.5B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C45
Q3_K_S	3	0.7 GB	Low	C45
NVFP4	4	0.8 GB	Medium	C45
Q4_K_M	4	0.9 GB	Medium	C45
Q5_K_M	5	1.1 GB	High	C45
Q6_K	6	1.2 GB	High	C45
Q8_0	8	1.6 GB	Very High	C46
F16Best for your GPU	16	3.1 GB	Maximum	C47

Get started

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

Run

lms load hf-lmstudio-community--yi-coder-1-5b-gguf && lms server start

升级选项

能流畅运行 Yi Coder 1.5B 的硬件

MacBook Pro M3 24GB经济之选

24 GB Unified (+8)

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

~$1,099 MSRP

MacBook Air M3 24GB最佳性价比

24 GB Unified (+8)

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

~$1,099 MSRP

Frequently asked questions

Can RTX 4090 Laptop 16GB run Yi Coder 1.5B?

Yes, RTX 4090 Laptop 16GB can run Yi Coder 1.5B with a C grade (Runs well). Expected decode speed: 24.0 tok/s.

How much VRAM does Yi Coder 1.5B need?

Yi Coder 1.5B (1.5B parameters) requires approximately 3.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 1.5B?

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

What speed will Yi Coder 1.5B run at on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Yi Coder 1.5B achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8067ms using Q4_K_M quantization.

Can RTX 4090 Laptop 16GB run Yi Coder 1.5B for coding?

For coding workloads, Yi Coder 1.5B on RTX 4090 Laptop 16GB receives a C grade with 24.0 tok/s and 1.1M context.

What context window can Yi Coder 1.5B use on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Yi Coder 1.5B can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4090 Laptop 16GB See all hardware for Yi Coder 1.5B

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