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URL: https://willitrunai.com/can-run/qwen-3-coder-next-on-rtx-5090-32gb

⇱ Qwen3-Coder-Next on RTX 5090 32GB? No — Alternatives

Can Qwen3-Coder-Next run on RTX 5090 32GB?

YES — With Q2_K

A82Great
○Estimated from fit model

Qwen3-Coder-Next needs ~37.1 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q2_K quantization, expect ~61 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: 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.

Qwen3-Coder-Next at Q4_K_M needs 54.7 GB — too much for RTX 5090 32GB (32.0 GB). Runs at Q2_K (37.1 GB) with low quality.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 54.7 GB, exceeds 32.0 GB available
54.7 GB required32.0 GB available
171% VRAM needed

22.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

20.8 tok/s

TTFT

9309 ms

Safe context

4K

Memory

54.7 GB / 32.0 GB

Offload

40%

Memory breakdown

Weights48.8 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom3.2 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsQwen3-Coder-Next on RTX 5090 32GB
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: 20.8 tok/s decode · 9.3s TTFT (warm) · 52 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 4.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy21.4 tok/s4940 ms4K
CodingFToo heavy20.8 tok/s9309 ms4K
Agentic CodingFToo heavy19.7 tok/s14291 ms4K
ReasoningFToo heavy20.8 tok/s11002 ms4K
RAGFToo heavy19.7 tok/s17864 ms4K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
31.2 GB
LowF0
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

Upgrade options

Hardware that runs Qwen3-Coder-Next well

👁 NVIDIA
RTX A6000 48GBBest value
48 GB VRAM (+16)
A
Makes the model fit on the accelerator instead of staying completely out of reach.21.2 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.

~$4,650 MSRP

👁 NVIDIA
RTX PRO 5000 Blackwell 48GBNVIDIA upgrade
48 GB VRAM (+16)
A
Makes the model fit on the accelerator instead of staying completely out of reach.42.2 tok/s decode

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

Raises estimated decode speed by about 103%.

~$4,999 MSRP

👁 NVIDIA
NVIDIA A16 64GBBudget pick
64 GB VRAM (+32)
S
Makes the model fit on the accelerator instead of staying completely out of reach.31.6 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.

~$6,500 MSRP

👁 NVIDIA
NVIDIA A100 80GBBiggest leap
80 GB VRAM (+48)2039 GB/s (+247)
S
Makes the model fit on the accelerator instead of staying completely out of reach.115.7 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.

~$15,000 MSRP

Frequently asked questions

See all results for RTX 5090 32GBSee all hardware for Qwen3-Coder-Next