Can Qwen3-Coder-Next run on RTX 6000 Ada 48GB?

Q: What should I upgrade first if Qwen3-Coder-Next feels slow on RTX 6000 Ada 48GB?

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

A80Great

Estimated from fit model

Qwen3-Coder-Next needs ~56.0 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: 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) — 56.0 GB, 28.9 tok/s, Very compromised (needs ~6.9 GB host RAM)

56.0 GB required48.0 GB available

117% VRAM needed

8.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~6.9 GB host RAM)

Decode

28.9 tok/s

TTFT

6708 ms

Safe context

Memory

56.0 GB / 48.0 GB

Offload

10%

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next on RTX 6000 Ada 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: 28.9 tok/s decode · 6.7s TTFT (warm) · 72 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 (needs ~6.4 GB host RAM)	29.7 tok/s	3559 ms	4K
Coding	A	Very compromised	26.5 tok/s	7295 ms	4K
Agentic Coding	A	Very compromised (needs ~8 GB host RAM)	27.3 tok/s	10303 ms	4K
Reasoning	A	Very compromised (needs ~6.9 GB host RAM)	28.9 tok/s	7928 ms	4K
RAG	A	Very compromised (needs ~8 GB host RAM)	27.3 tok/s

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	31.2 GB	Low	S88
Q3_K_S	3	39.2 GB	Low	F0

Get started

Copy-paste commands to run Qwen3-Coder-Next on your machine.

Run

ollama run qwen3-coder-next

Frequently asked questions

Can RTX 6000 Ada 48GB run Qwen3-Coder-Next?

Yes, RTX 6000 Ada 48GB can run Qwen3-Coder-Next with a A grade (Very compromised). Expected decode speed: 26.5 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 56.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder-Next?

The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder-Next run at on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Qwen3-Coder-Next achieves approximately 26.5 tokens per second decode speed with a time-to-first-token of 7295ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on RTX 6000 Ada 48GB receives a A grade with 26.5 tok/s and 4K context.

What context window can Qwen3-Coder-Next use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Qwen3-Coder-Next can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3-Coder-Next feels slow on RTX 6000 Ada 48GB?

See all results for RTX 6000 Ada 48GB See all hardware for Qwen3-Coder-Next

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