Can Yi Coder 1.5B run on NVIDIA L4 24GB?

YES — Runs Great

C41Usable

Estimated from fit model

Yi Coder 1.5B needs ~4.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 4.4 GB, 24.0 tok/s, Runs well

4.4 GB required24.0 GB available

18% VRAM used

Fit status

Runs well

Decode

24.0 tok/s

TTFT

8067 ms

Safe context

1.8M

Memory

4.4 GB / 24.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B on NVIDIA L4 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: 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	1.6M
Coding	C	Runs well	24.0 tok/s	8067 ms	1.8M
Agentic Coding	C	Runs well	24.0 tok/s	11733 ms	1.8M
Reasoning	C	Runs well	24.0 tok/s	9533 ms	1.8M
RAG	C	Runs well	24.0 tok/s	14667 ms	1.8M

Quantization options

How Yi Coder 1.5B (1.5B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C43
Q3_K_S	3	0.7 GB	Low	C43
NVFP4	4	0.8 GB	Medium	C43
Q4_K_M	4	0.9 GB	Medium	C43
Q5_K_M	5	1.1 GB	High	C43
Q6_K	6	1.2 GB	High	C44
Q8_0	8	1.6 GB	Very High	C44
F16Best for your GPU	16	3.1 GB	Maximum	C44

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

Upgrade options

Hardware that runs Yi Coder 1.5B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1492)

Adds memory headroom for longer context windows and future model growth.28.5 tok/s decode

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

~$1,999 MSRP

MacBook Pro M3 Pro 36GBBest value

36 GB Unified (+12)

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

~$1,999 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Yi Coder 1.5B?

Yes, NVIDIA L4 24GB 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 4.4 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, 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 NVIDIA L4 24GB run Yi Coder 1.5B for coding?

For coding workloads, Yi Coder 1.5B on NVIDIA L4 24GB receives a C grade with 24.0 tok/s and 1.8M context.

What context window can Yi Coder 1.5B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Yi Coder 1.5B can safely use up to 1.8M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for Yi Coder 1.5B

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<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--yi-coder-1-5b-gguf-on-l4-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

24.0 tok/s

4400 ms

1.6M

Coding

Runs well

24.0 tok/s

8067 ms

1.8M

Agentic Coding

Runs well

24.0 tok/s

11733 ms

1.8M

Reasoning

Runs well

24.0 tok/s

9533 ms

1.8M

RAG

Runs well

24.0 tok/s

14667 ms

1.8M

Quantization options

How Yi Coder 1.5B (1.5B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C43
Q3_K_S	3	0.7 GB	Low	C43
NVFP4	4	0.8 GB	Medium	C43
Q4_K_M	4	0.9 GB	Medium	C43
Q5_K_M	5	1.1 GB	High	C43
Q6_K	6	1.2 GB	High	C44
Q8_0	8	1.6 GB	Very High	C44
F16Best for your GPU	16	3.1 GB	Maximum	C44

Frequently asked questions

Can NVIDIA L4 24GB run Yi Coder 1.5B?

Yes, NVIDIA L4 24GB 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 4.4 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, 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 NVIDIA L4 24GB run Yi Coder 1.5B for coding?

For coding workloads, Yi Coder 1.5B on NVIDIA L4 24GB receives a C grade with 24.0 tok/s and 1.8M context.

What context window can Yi Coder 1.5B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Yi Coder 1.5B can safely use up to 1.8M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.