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

YES — Runs Great

C48Usable

Estimated from fit model

Yi Coder 9B Chat needs ~10.1 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~36 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) — 10.1 GB, 35.5 tok/s, Runs well

10.1 GB required24.0 GB available

42% VRAM used

Fit status

Runs well

Decode

35.5 tok/s

TTFT

5451 ms

Safe context

226K

Memory

10.1 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat 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: 35.5 tok/s decode · 5.5s TTFT (warm) · 89 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	35.5 tok/s	2973 ms	226K
Coding	C	Runs well	35.5 tok/s	5451 ms	226K
Agentic Coding	C	Runs well	35.5 tok/s	7928 ms	226K
Reasoning	C	Runs well	35.5 tok/s	6442 ms	226K
RAG	C	Runs well	35.5 tok/s	9910 ms	226K

Quantization options

How Yi Coder 9B Chat (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	C45
Q3_K_S	3	4.4 GB	Low	C45
NVFP4	4	5.0 GB	Medium	C46
Q4_K_M	4	5.5 GB	Medium	C46
Q5_K_M	5	6.5 GB	High	C46
Q6_K	6	7.4 GB	High	C47
Q8_0	8	9.6 GB	Very High	C48
F16Best for your GPU	16	18.5 GB	Maximum	C50

Get started

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

Run

lms load hf-maziyarpanahi--yi-coder-9b-chat-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien Yi Coder 9B Chat

RTX 5090 32GBOpción económica

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

Sube la velocidad estimada de decodificación alrededor de un 255%.126 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 255%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMejor relación calidad-precio

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

Sube la velocidad estimada de decodificación alrededor de un 255%.126 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 255%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

RTX 5000 Ada 32GBMejora NVIDIA

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

Sube la velocidad estimada de decodificación alrededor de un 136%.83.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 136%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$4,000 MSRP

Frequently asked questions

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

Yes, NVIDIA L4 24GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 35.5 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 10.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 9B Chat?

The recommended quantization for Yi Coder 9B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 9B Chat run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Yi Coder 9B Chat achieves approximately 35.5 tokens per second decode speed with a time-to-first-token of 5451ms using Q4_K_M quantization.

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

For coding workloads, Yi Coder 9B Chat on NVIDIA L4 24GB receives a C grade with 35.5 tok/s and 226K context.

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

On NVIDIA L4 24GB, Yi Coder 9B Chat can safely use up to 226K 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 9B Chat

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