Can Qwen3-Coder-Next run on RTX A4000 16GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Qwen3-Coder-Next needs ~53.1 GB but RTX A4000 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 53.1 GB, exceeds 16.0 GB available

53.1 GB required16.0 GB available

332% VRAM needed

37.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.2 tok/s

TTFT

60946 ms

Safe context

Memory

53.1 GB / 16.0 GB

Offload

70%

Memory breakdown

Weights48.8 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-Next on RTX A4000 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: 3.2 tok/s decode · 60.9s TTFT (warm) · 8 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 53.1 GB, but this setup only exposes 16.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.9 tok/s	36152 ms	4K
Coding	F	Too heavy	2.9 tok/s	66278 ms	4K
Agentic Coding	F	Too heavy	2.9 tok/s	96405 ms	4K
Reasoning	F	Too heavy	2.9 tok/s	78329 ms	4K
RAG	F	Too heavy	2.9 tok/s	120506 ms	4K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on RTX A4000 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	F0
Q3_K_S	3	39.2 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Qwen3-Coder-Next well

👁 NVIDIA
RTX A6000 48GBBest value

48 GB VRAM (+32)768 GB/s (+320)

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.

Raises estimated decode speed by about 563%.

~$4,650 MSRP

👁 NVIDIA
RTX PRO 5000 Blackwell 48GBNVIDIA upgrade

48 GB VRAM (+32)1344 GB/s (+896)

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 1219%.

~$4,999 MSRP

👁 NVIDIA
NVIDIA A16 64GBBudget pick

64 GB VRAM (+48)600 GB/s (+152)

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 (+64)2039 GB/s (+1591)

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 A4000 16GB See all hardware for Qwen3-Coder-Next

URL: https://willitrunai.com/can-run/qwen-3-coder-next-on-a4000-16gb

⇱

Can Qwen3-Coder-Next run on RTX A4000 16GB?

NO — Won't Fit

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Hardware that runs Qwen3-Coder-Next well

Frequently asked questions