Can Qwen3-Coder 30B A3B Instruct run on RTX 4000 Ada 20GB?

Q: What should I upgrade first if Qwen3-Coder 30B A3B Instruct feels slow on RTX 4000 Ada 20GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

A82Great

Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~23.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 23.0 GB, 23.8 tok/s, Very compromised (needs ~2.4 GB host RAM)

23.0 GB required20.0 GB available

115% VRAM needed

3.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.4 GB host RAM)

Decode

23.8 tok/s

TTFT

8136 ms

Safe context

Memory

23.0 GB / 20.0 GB

Offload

10%

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on RTX 4000 Ada 20GB
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: 23.8 tok/s decode · 8.1s TTFT (warm) · 60 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised	23.4 tok/s	4508 ms	4K
Coding	A	Very compromised	21.9 tok/s	8848 ms	4K
Agentic Coding	F	Too heavy	19.2 tok/s	14659 ms	4K
Reasoning	A	Very compromised	21.9 tok/s	10457 ms	4K
RAG	F	Too heavy	19.2 tok/s	18324 ms	4K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	S93
Q3_K_SBest for your GPU	3	14.9 GB	Low	S93

Get started

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

Run

ollama run qwen3-coder

Frequently asked questions

Can RTX 4000 Ada 20GB run Qwen3-Coder 30B A3B Instruct?

Yes, RTX 4000 Ada 20GB can run Qwen3-Coder 30B A3B Instruct with a A grade (Very compromised). Expected decode speed: 21.9 tok/s.

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

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 23.0 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Qwen3-Coder 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder 30B A3B Instruct run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Qwen3-Coder 30B A3B Instruct achieves approximately 21.9 tokens per second decode speed with a time-to-first-token of 8848ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on RTX 4000 Ada 20GB receives a A grade with 21.9 tok/s and 4K context.

What context window can Qwen3-Coder 30B A3B Instruct use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Qwen3-Coder 30B A3B Instruct can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for Qwen3-Coder 30B A3B Instruct

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