Can Qwen 3.5 9B run on Quadro RTX 8000 48GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Qwen 3.5 9B needs ~13.7 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~91 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) — 13.7 GB, 90.8 tok/s, Runs well

13.7 GB required48.0 GB available

29% VRAM used

Fit status

Runs well

Decode

90.8 tok/s

TTFT

2132 ms

Safe context

131K

Memory

13.7 GB / 48.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B 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: 90.8 tok/s decode · 2.1s TTFT (warm) · 227 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	S	Runs well	90.8 tok/s	1163 ms	131K
Coding	S	Runs well	90.8 tok/s	2132 ms	131K
Agentic Coding	S	Runs well	90.8 tok/s	3102 ms	131K
Reasoning	S	Runs well	90.8 tok/s	2520 ms	131K
RAG	S	Runs well	90.8 tok/s	3877 ms	131K

Inference speed

Qwen 3.5 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.5 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	126.0

Quantization options

How Qwen 3.5 9B (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	A83
Q3_K_S	3	4.4 GB	Low	A83
NVFP4	4

Get started

Copy-paste commands to run Qwen 3.5 9B on your machine.

Run

ollama run qwen3.5:9b

Your hardware

More models your Quadro RTX 8000 48GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.1 tok/s
Qwen 3.5 27B	27B	S	30.4 tok/s

Frequently asked questions

Can Quadro RTX 8000 48GB run Qwen 3.5 9B?

Yes, Quadro RTX 8000 48GB can run Qwen 3.5 9B with a S grade (Runs well). Expected decode speed: 90.8 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 9B?

The recommended quantization for Qwen 3.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 9B run at on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Qwen 3.5 9B achieves approximately 90.8 tokens per second decode speed with a time-to-first-token of 2132ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on Quadro RTX 8000 48GB receives a S grade with 90.8 tok/s and 131K context.

What context window can Qwen 3.5 9B use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Qwen 3.5 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Quadro RTX 8000 48GB See all hardware for Qwen 3.5 9B

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