Can Yi Coder 9B run on NVIDIA L4 24GB?

YES — Runs Great

B61Good

Estimated from fit model

Yi Coder 9B needs ~10.6 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~39 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) — 10.6 GB, 38.6 tok/s, Runs well

10.6 GB required24.0 GB available

44% VRAM used

Fit status

Runs well

Decode

38.6 tok/s

TTFT

5012 ms

Safe context

131K

Memory

10.6 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsYi Coder 9B 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: 38.6 tok/s decode · 5.0s TTFT (warm) · 97 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	38.6 tok/s	2734 ms	131K
Coding	B	Runs well	38.6 tok/s	5012 ms	131K
Agentic Coding	B	Runs well	38.6 tok/s	7290 ms	131K
Reasoning	B	Runs well	38.6 tok/s	5923 ms	131K
RAG	B	Runs well	38.6 tok/s	9113 ms	131K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B57
Q3_K_S	3	4.4 GB	Low	B57
NVFP4	4	5.0 GB	Medium	B58
Q4_K_M	4	5.5 GB	Medium	B58
Q5_K_M	5	6.5 GB	High	B58
Q6_K	6	7.4 GB	High	B59
Q8_0	8	9.6 GB	Very High	B60
F16Best for your GPU	16	18.5 GB	Maximum	B62

Get started

Copy-paste commands to run Yi Coder 9B on your machine.

Run

lms load Yi-Coder-9B-Chat && lms server start

升级选项

能流畅运行 Yi Coder 9B 的硬件

RTX 5090 32GB经济之选

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

Raises estimated decode speed by about 226%.126 tok/s 解码

Raises estimated decode speed by about 226%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GB最佳性价比

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 226%.126 tok/s 解码

Raises estimated decode speed by about 226%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA 升级

32 GB VRAM (+8)576 GB/s (+276)

Raises estimated decode speed by about 137%.91.3 tok/s 解码

Raises estimated decode speed by about 137%.

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

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Yi Coder 9B?

Yes, NVIDIA L4 24GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 38.6 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 10.6 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Yi Coder 9B achieves approximately 38.6 tokens per second decode speed with a time-to-first-token of 5012ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on NVIDIA L4 24GB receives a B grade with 38.6 tok/s and 131K context.

What context window can Yi Coder 9B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Yi Coder 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for Yi Coder 9B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/yi-coder-9b-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: