Can Yi Coder 9B Chat run on NVIDIA A2 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

Yi Coder 9B Chat needs ~9.3 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 9.3 GB, 28.4 tok/s, Runs well

9.3 GB required16.0 GB available

58% VRAM used

Fit status

Runs well

Decode

28.4 tok/s

TTFT

6813 ms

Safe context

117K

Memory

9.3 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on NVIDIA A2 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: 28.4 tok/s decode · 6.8s TTFT (warm) · 71 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	28.4 tok/s	3716 ms	117K
Coding	C	Runs well	28.4 tok/s	6813 ms	117K
Agentic Coding	C	Runs well	28.4 tok/s	9910 ms	117K
Reasoning	C	Runs well	28.4 tok/s	8052 ms	117K
RAG	C	Runs well	28.4 tok/s	12388 ms	117K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C47
Q3_K_S	3	4.4 GB	Low	C48
NVFP4	4

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

Upgrade options

Hardware that runs Yi Coder 9B Chat well

RTX 3090 24GBBudget pick

24 GB VRAM (+8)936 GB/s (+736)

Raises estimated decode speed by about 320%.119.3 tok/s decode

Raises estimated decode speed by about 320%.

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

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+8)1008 GB/s (+808)

Raises estimated decode speed by about 344%.126 tok/s decode

Raises estimated decode speed by about 344%.

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

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+472)

Raises estimated decode speed by about 262%.102.8 tok/s decode

Raises estimated decode speed by about 262%.

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

~$1,599 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run Yi Coder 9B Chat?

Yes, NVIDIA A2 16GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 28.4 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 9.3 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 A2 16GB?

On NVIDIA A2 16GB, Yi Coder 9B Chat achieves approximately 28.4 tokens per second decode speed with a time-to-first-token of 6813ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on NVIDIA A2 16GB receives a C grade with 28.4 tok/s and 117K context.

What context window can Yi Coder 9B Chat use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Yi Coder 9B Chat can safely use up to 117K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for Yi Coder 9B Chat

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