Can Qwen3-Coder-Next run on NVIDIA A16 64GB?

YES — Tight Fit

S90Excellent

Estimated from fit model

Qwen3-Coder-Next needs ~57.9 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~32 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced

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) — 57.9 GB, 31.6 tok/s, Tight fit

57.9 GB required64.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

31.6 tok/s

TTFT

6126 ms

Safe context

83K

Memory

57.9 GB / 64.0 GB

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next on NVIDIA A16 64GB
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: 31.6 tok/s decode · 6.1s TTFT (warm) · 79 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	31.6 tok/s	3341 ms	83K
Coding	S	Tight fit	31.6 tok/s	6126 ms	83K
Agentic Coding	S	Tight fit	31.6 tok/s	8910 ms	83K
Reasoning	S	Tight fit	31.6 tok/s	7240 ms	83K
RAG	S	Tight fit	31.6 tok/s	11138 ms	83K

Inference speed

Qwen3-Coder-Next inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen3-Coder-Next at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~49 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	48.9	Fits
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	43.1	Heavy offload
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	40.7	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	39.3	Heavy offload
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	38.6	Fits
2× RTX 3090 24GB	48 GB	Q4_K_M	33.6	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	30.2	Fits
4× RTX 3060 12GB	48 GB	Q4_K_M	29.6	Heavy offload
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	21.7	Too big
RTX 5090 32GB	32 GB	Q4_K_M	20.8	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	16.6	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	15.3	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	10.8	Too big
RTX 4090 24GB	24 GB	Q4_K_M	7.8	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	7.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	6.6	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	6.2	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.8	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.4	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	S87
Q3_K_S	3	39.2 GB	Low	S88
NVFP4	4	44.8 GB	Medium	S88
Q4_K_MBest for your GPU	4	48.8 GB	Medium	S88
Q5_K_M	5	57.6 GB	High	F0
Q6_K	6	65.6 GB	High	F0
Q8_0	8	85.6 GB	Very High	F0
F16	16	164.0 GB	Maximum	F0

Get started

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

Run

ollama run qwen3-coder-next

Frequently asked questions

Can NVIDIA A16 64GB run Qwen3-Coder-Next?

Yes, NVIDIA A16 64GB can run Qwen3-Coder-Next with a S grade (Tight fit). Expected decode speed: 31.6 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 57.9 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 NVIDIA A16 64GB?

On NVIDIA A16 64GB, Qwen3-Coder-Next achieves approximately 31.6 tokens per second decode speed with a time-to-first-token of 6126ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on NVIDIA A16 64GB receives a S grade with 31.6 tok/s and 83K context.

What context window can Qwen3-Coder-Next use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Qwen3-Coder-Next can safely use up to 83K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for Qwen3-Coder-Next

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-3-coder-next-on-a16-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

31.6 tok/s

3341 ms

83K

Coding

Tight fit

31.6 tok/s

6126 ms

83K

Agentic Coding

Tight fit

31.6 tok/s

8910 ms

83K

Reasoning

Tight fit

31.6 tok/s

7240 ms

83K

RAG

Tight fit

31.6 tok/s

11138 ms

83K

Qwen3-Coder-Next inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

48.9

Fits

2× RX 7900 XTX 24GB

48 GB

Q4_K_M

43.1

Heavy offload

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

40.7

Fits

2× RTX 4090 24GB

48 GB

Q4_K_M

39.3

Heavy offload

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

38.6

Fits

2× RTX 3090 24GB

48 GB

Q4_K_M

33.6

Heavy offload

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

30.2

Fits

4× RTX 3060 12GB

48 GB

Q4_K_M

29.6

Heavy offload

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

21.7

Too big

RTX 5090 32GB

32 GB

Q4_K_M

20.8

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

16.6

Too big

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

15.3

Too big

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

10.8

Too big

RTX 4090 24GB

24 GB

Q4_K_M

7.8

Too big

RX 7900 XTX 24GB

24 GB

Q4_K_M

7.0

Too big

RTX 3090 24GB

24 GB

Q4_K_M

6.6

Too big

RTX 4080 Super 16GB

16 GB

Q4_K_M

6.2

Too big

RTX 4070 12GB

12 GB

Q4_K_M

3.8

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.4

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	S87
Q3_K_S	3	39.2 GB	Low	S88
NVFP4	4	44.8 GB	Medium	S88
Q4_K_MBest for your GPU	4	48.8 GB	Medium	S88
Q5_K_M	5	57.6 GB	High	F0
Q6_K	6	65.6 GB	High	F0
Q8_0	8	85.6 GB	Very High	F0
F16	16	164.0 GB	Maximum	F0

Frequently asked questions

Can NVIDIA A16 64GB run Qwen3-Coder-Next?

Yes, NVIDIA A16 64GB can run Qwen3-Coder-Next with a S grade (Tight fit). Expected decode speed: 31.6 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 57.9 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 NVIDIA A16 64GB?

On NVIDIA A16 64GB, Qwen3-Coder-Next achieves approximately 31.6 tokens per second decode speed with a time-to-first-token of 6126ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on NVIDIA A16 64GB receives a S grade with 31.6 tok/s and 83K context.

What context window can Qwen3-Coder-Next use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Qwen3-Coder-Next can safely use up to 83K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.