Can Yi Coder 9B Chat run on NVIDIA A100 40GB?

YES — Runs Great

C48Usable

Estimated from fit model

Yi Coder 9B Chat needs ~11.7 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~126 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 11.7 GB, 126.0 tok/s, Runs well

11.7 GB required40.0 GB available

29% VRAM used

Fit status

Runs well

Decode

126.0 tok/s

TTFT

1537 ms

Safe context

445K

Memory

11.7 GB / 40.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on NVIDIA A100 40GB
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: 126.0 tok/s decode · 1.5s TTFT (warm) · 315 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	126.0 tok/s	838 ms	445K
Coding	C	Runs well	126.0 tok/s	1537 ms	445K
Agentic Coding	C	Runs well	126.0 tok/s	2235 ms	445K
Reasoning	C	Runs well	126.0 tok/s	1816 ms	445K
RAG	C	Runs well	126.0 tok/s	2794 ms	445K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C42
Q3_K_S	3	4.4 GB	Low	C42
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

Mac Studio M2 Ultra 64GBBudget pick

64 GB Unified (+24)

This setup is broadly balanced for this model.84.5 tok/s decode

~$3,999 MSRP

Mac Studio M1 Ultra 64GBBest value

64 GB Unified (+24)

This setup is broadly balanced for this model.80.1 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A100 40GB run Yi Coder 9B Chat?

Yes, NVIDIA A100 40GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 126.0 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 11.7 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 A100 40GB?

On NVIDIA A100 40GB, Yi Coder 9B Chat achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on NVIDIA A100 40GB receives a C grade with 126.0 tok/s and 445K context.

What context window can Yi Coder 9B Chat use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Yi Coder 9B Chat can safely use up to 445K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for Yi Coder 9B Chat

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