Can Yi Coder 9B Chat run on NVIDIA B200 180GB?

YES — Runs Great

C45Usable

Estimated from fit model

Yi Coder 9B Chat needs ~25.7 GB VRAM. NVIDIA B200 180GB has 180.0 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) — 25.7 GB, 126.0 tok/s, Runs well

25.7 GB required180.0 GB available

14% VRAM used

Fit status

Runs well

Decode

126.0 tok/s

TTFT

1537 ms

Safe context

2.4M

Memory

25.7 GB / 180.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on NVIDIA B200 180GB
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	2.4M
Coding	C	Runs well	126.0 tok/s	1537 ms	2.4M
Agentic Coding	C	Runs well	126.0 tok/s	2235 ms	2.4M
Reasoning	C	Runs well	126.0 tok/s	1816 ms	2.4M
RAG	C	Runs well	126.0 tok/s	2794 ms	2.4M

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	D37
Q3_K_S	3	4.4 GB	Low	D37
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 M3 Ultra 256GBBudget pick

256 GB Unified (+76)

This setup is broadly balanced for this model.101.4 tok/s decode

~$6,999 MSRP

Frequently asked questions

Can NVIDIA B200 180GB run Yi Coder 9B Chat?

Yes, NVIDIA B200 180GB 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 25.7 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 B200 180GB?

On NVIDIA B200 180GB, 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 B200 180GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on NVIDIA B200 180GB receives a C grade with 126.0 tok/s and 2.4M context.

What context window can Yi Coder 9B Chat use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Yi Coder 9B Chat can safely use up to 2.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB See all hardware for Yi Coder 9B Chat

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