Can Yi Coder 9B Chat run on RTX A2000 12GB?

YES — Runs Great

C54Usable

Estimated from fit model

Yi Coder 9B Chat needs ~8.9 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~41 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) — 8.9 GB, 40.9 tok/s, Runs well

8.9 GB required12.0 GB available

74% VRAM used

Fit status

Runs well

Decode

40.9 tok/s

TTFT

4731 ms

Safe context

62K

Memory

8.9 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on RTX A2000 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: 40.9 tok/s decode · 4.7s TTFT (warm) · 102 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	40.9 tok/s	2581 ms	62K
Coding	C	Runs well	40.9 tok/s	4731 ms	62K
Agentic Coding	C	Tight fit	40.9 tok/s	6882 ms	62K
Reasoning	C	Runs well	40.9 tok/s	5592 ms	62K
RAG	C	Tight fit	40.9 tok/s	8603 ms	62K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C51
NVFP4	4	5.0 GB	Medium	C52
Q4_K_M	4	5.5 GB	Medium	C53
Q5_K_M	5	6.5 GB	High	C52
Q6_KBest for your GPU	6	7.4 GB	High	C52
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

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 5070 Ti 16GBOpción económica

16 GB VRAM (+4)896 GB/s (+608)

Sube la velocidad estimada de decodificación alrededor de un 156%.104.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 156%.

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

~$749 MSRP

RTX 4070 Ti Super 16GBMejor relación calidad-precio

16 GB VRAM (+4)672 GB/s (+384)

Sube la velocidad estimada de decodificación alrededor de un 139%.97.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 139%.

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

~$799 MSRP

RTX 4080 Super 16GBMejora NVIDIA

16 GB VRAM (+4)736 GB/s (+448)

Sube la velocidad estimada de decodificación alrededor de un 172%.111.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 172%.

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

~$999 MSRP

Frequently asked questions

Can RTX A2000 12GB run Yi Coder 9B Chat?

Yes, RTX A2000 12GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 40.9 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 8.9 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 RTX A2000 12GB?

On RTX A2000 12GB, Yi Coder 9B Chat achieves approximately 40.9 tokens per second decode speed with a time-to-first-token of 4731ms using Q4_K_M quantization.

Can RTX A2000 12GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on RTX A2000 12GB receives a C grade with 40.9 tok/s and 62K context.

What context window can Yi Coder 9B Chat use on RTX A2000 12GB?

On RTX A2000 12GB, Yi Coder 9B Chat can safely use up to 62K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX A2000 12GB See all hardware for Yi Coder 9B Chat

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-coder-9b-chat-gguf-on-a2000-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: