Will It Run AI

Can Qwen3-Coder-Next run on RTX A6000 48GB?

BARELY — Tight on Memory

A79Great
Estimated from fit model

Qwen3-Coder-Next needs ~56.0 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
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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, 21.4 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

21.4 tok/s

TTFT

9047 ms

Safe context

4K

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 A6000 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: 21.4 tok/s decode · 9.0s TTFT (warm) · 54 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 6.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatAVery compromised (needs ~6.4 GB host RAM)22.0 tok/s4800 ms4K
CodingAVery compromised (needs ~6.9 GB host RAM)21.4 tok/s9047 ms4K
Agentic CodingAVery compromised (needs ~8 GB host RAM)20.3 tok/s13895 ms4K
ReasoningAVery compromised (needs ~6.9 GB host RAM)21.4 tok/s10692 ms4K
RAGAVery compromised (needs ~8 GB host RAM)20.3 tok/s17369 ms4K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
31.2 GB
LowS88
Q3_K_S
3
39.2 GB
LowF0
NVFP4
4
44.8 GB
MediumF0
Q4_K_M
4
48.8 GB
MediumF0
Q5_K_M
5
57.6 GB
HighF0
Q6_K
6
65.6 GB
HighF0
Q8_0
8
85.6 GB
Very HighF0
F16
16
164.0 GB
MaximumF0

Get started

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

Run

ollama run qwen3-coder-next

Frequently asked questions

Can RTX A6000 48GB run Qwen3-Coder-Next?

Yes, RTX A6000 48GB can run Qwen3-Coder-Next with a A grade (Very compromised (needs ~6.9 GB host RAM)). Expected decode speed: 21.4 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 A6000 48GB?

On RTX A6000 48GB, Qwen3-Coder-Next achieves approximately 21.4 tokens per second decode speed with a time-to-first-token of 9047ms using Q4_K_M quantization.

Can RTX A6000 48GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on RTX A6000 48GB receives a A grade with 21.4 tok/s and 4K context.

What context window can Qwen3-Coder-Next use on RTX A6000 48GB?

On RTX A6000 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 A6000 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.

See all results for RTX A6000 48GBSee all hardware for Qwen3-Coder-Next
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