Can Qwen3-Coder 30B A3B Instruct run on RTX 6000 Ada Laptop 16GB?

Q: What context window can Qwen3-Coder 30B A3B Instruct use on RTX 6000 Ada Laptop 16GB?

On RTX 6000 Ada Laptop 16GB, Qwen3-Coder 30B A3B Instruct can safely use up to 14K tokens of context at Q2_K quantization. The model's official context limit is 256K, but available memory constrains the safe maximum.

Q: What should I upgrade first if Qwen3-Coder 30B A3B Instruct feels slow on RTX 6000 Ada Laptop 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Q2_K

S96Excellent

Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~16.2 GB VRAM. RTX 6000 Ada Laptop 16GB has 16.0 GB. With Q2_K quantization, expect ~62 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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.

Qwen3-Coder 30B A3B Instruct at Q4_K_M needs 22.9 GB — too much for RTX 6000 Ada Laptop 16GB (16.0 GB). Runs at Q2_K (16.2 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.9 GB, exceeds 16.0 GB available

22.9 GB required16.0 GB available

143% VRAM needed

6.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

22.5 tok/s

TTFT

8612 ms

Safe context

Memory

22.9 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-Coder 30B A3B Instruct on RTX 6000 Ada Laptop 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: 22.5 tok/s decode · 8.6s TTFT (warm) · 56 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	22.1 tok/s	4770 ms	4K
Coding	F	Too heavy	22.5 tok/s	8612 ms	4K
Agentic Coding	F	Too heavy	19.7 tok/s	14275 ms	4K
Reasoning	F	Too heavy	22.5 tok/s	10177 ms	4K
RAG	F	Too heavy	19.7 tok/s	17844 ms	4K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on RTX 6000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	F0
Q3_K_S	3	14.9 GB	Low	F0
NVFP4	4

Get started

Copy-paste commands to run Qwen3-Coder 30B A3B Instruct on your machine.

Run

ollama run qwen3-coder

Upgrade options

Hardware that runs Qwen3-Coder 30B A3B Instruct well

RTX 4000 Ada 20GBBest value

20 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.23.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,250 MSRP

RTX 3090 24GBBudget pick

24 GB VRAM (+8)936 GB/s (+360)

Makes the model fit on the accelerator instead of staying completely out of reach.99.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+8)1008 GB/s (+432)

Makes the model fit on the accelerator instead of staying completely out of reach.115.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

Can RTX 6000 Ada Laptop 16GB run Qwen3-Coder 30B A3B Instruct?

Yes, RTX 6000 Ada Laptop 16GB can run Qwen3-Coder 30B A3B Instruct at Q2_K quantization (Runs with offload (needs ~0.1 GB host RAM)). The recommended Q4_K_M requires 22.9 GB which exceeds available memory, but at Q2_K it needs only 16.2 GB. Expected decode speed: 62.1 tok/s.

How much VRAM does Qwen3-Coder 30B A3B Instruct need?

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 22.9 GB at Q4_K_M quantization. On RTX 6000 Ada Laptop 16GB, it fits at Q2_K using 16.2 GB.

What is the best quantization for Qwen3-Coder 30B A3B Instruct?

The recommended quantization is Q4_K_M, but on RTX 6000 Ada Laptop 16GB the best fitting quantization is Q2_K, which uses 16.2 GB.

What speed will Qwen3-Coder 30B A3B Instruct run at on RTX 6000 Ada Laptop 16GB?

On RTX 6000 Ada Laptop 16GB, Qwen3-Coder 30B A3B Instruct achieves approximately 62.1 tokens per second decode speed with a time-to-first-token of 3118ms using Q2_K quantization.

Can RTX 6000 Ada Laptop 16GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on RTX 6000 Ada Laptop 16GB receives a F grade with 22.5 tok/s and 4K context.

What context window can Qwen3-Coder 30B A3B Instruct use on RTX 6000 Ada Laptop 16GB?

See all results for RTX 6000 Ada Laptop 16GB See all hardware for Qwen3-Coder 30B A3B Instruct

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