Can Yi Coder 9B Chat run on Quadro RTX 8000 48GB?

YES — Runs Great

C47Usable

Estimated from fit model

Yi Coder 9B Chat needs ~12.5 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~85 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 12.5 GB, 84.5 tok/s, Runs well

12.5 GB required48.0 GB available

26% VRAM used

Fit status

Runs well

Decode

84.5 tok/s

TTFT

2292 ms

Safe context

554K

Memory

12.5 GB / 48.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on Quadro RTX 8000 48GB
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: 84.5 tok/s decode · 2.3s TTFT (warm) · 211 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	84.5 tok/s	1250 ms	554K
Coding	C	Runs well	84.5 tok/s	2292 ms	554K
Agentic Coding	C	Runs well	84.5 tok/s	3334 ms	554K
Reasoning	C	Runs well	84.5 tok/s	2709 ms	554K
RAG	C	Runs well	84.5 tok/s	4168 ms	554K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 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	5.0 GB	Medium	C42
Q4_K_M	4	5.5 GB	Medium	C42
Q5_K_M	5	6.5 GB	High	C42
Q6_K	6	7.4 GB	High	C42
Q8_0	8	9.6 GB	Very High	C43
F16Best for your GPU	16	18.5 GB	Maximum	C45

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

アップグレードオプション

Yi Coder 9B Chatを快適に動かすハードウェア

Mac Studio M3 Ultra 96GBコスパ重視

96 GB Unified (+48)819 GB/s (+147)

Adds memory headroom for longer context windows and future model growth.101.4 tok/sデコード

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

〜$3,999 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run Yi Coder 9B Chat?

Yes, Quadro RTX 8000 48GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 84.5 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 12.5 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Yi Coder 9B Chat achieves approximately 84.5 tokens per second decode speed with a time-to-first-token of 2292ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on Quadro RTX 8000 48GB receives a C grade with 84.5 tok/s and 554K context.

What context window can Yi Coder 9B Chat use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Yi Coder 9B Chat can safely use up to 554K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Quadro RTX 8000 48GB See all hardware for Yi Coder 9B Chat

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