Can Qwen3-Coder-Next run on NVIDIA A100 40GB?

YES — With Q3_K_S

A82Great

Estimated from fit model

Qwen3-Coder-Next needs ~45.9 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q3_K_S quantization, expect ~57 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: 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.

Qwen3-Coder-Next at Q4_K_M needs 55.5 GB — too much for NVIDIA A100 40GB (40.0 GB). Runs at Q3_K_S (45.9 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 55.5 GB, exceeds 40.0 GB available

55.5 GB required40.0 GB available

139% VRAM needed

15.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

33.2 tok/s

TTFT

5823 ms

Safe context

Memory

55.5 GB / 40.0 GB

Offload

30%

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-Coder-Next on NVIDIA A100 40GB
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: 33.2 tok/s decode · 5.8s TTFT (warm) · 83 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 5.0 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	F	Too heavy	34.2 tok/s	3089 ms	4K
Coding	F	Too heavy	33.2 tok/s	5823 ms	4K
Agentic Coding	F	Too heavy	31.5 tok/s	8948 ms	4K
Reasoning	F	Too heavy	33.2 tok/s	6882 ms	4K
RAG	F	Too heavy	31.5 tok/s	11185 ms	4K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on NVIDIA A100 40GB (40.0 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	44.8 GB	Medium	F0
Q4_K_M	4	48.8 GB	Medium	F0
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

Opciones de mejora

Hardware que ejecuta bien Qwen3-Coder-Next

RTX A6000 48GBMejor relación calidad-precio

48 GB VRAM (+8)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.21.2 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBMejora NVIDIA

48 GB VRAM (+8)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.42.2 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 27%.

~$4,999 MSRP

NVIDIA A16 64GBOpción económica

64 GB VRAM (+24)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.31.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$6,500 MSRP

NVIDIA A100 80GBMayor salto

80 GB VRAM (+40)2039 GB/s (+484)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.115.7 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$15,000 MSRP

Frequently asked questions

Can NVIDIA A100 40GB run Qwen3-Coder-Next?

Yes, NVIDIA A100 40GB can run Qwen3-Coder-Next at Q3_K_S quantization (Very compromised (needs ~5 GB host RAM)). The recommended Q4_K_M requires 55.5 GB which exceeds available memory, but at Q3_K_S it needs only 45.9 GB. Expected decode speed: 57.4 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 55.5 GB at Q4_K_M quantization. On NVIDIA A100 40GB, it fits at Q3_K_S using 45.9 GB.

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

The recommended quantization is Q4_K_M, but on NVIDIA A100 40GB the best fitting quantization is Q3_K_S, which uses 45.9 GB.

What speed will Qwen3-Coder-Next run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Qwen3-Coder-Next achieves approximately 57.4 tokens per second decode speed with a time-to-first-token of 3371ms using Q3_K_S quantization.

Can NVIDIA A100 40GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on NVIDIA A100 40GB receives a F grade with 33.2 tok/s and 4K context.

What context window can Qwen3-Coder-Next use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Qwen3-Coder-Next can safely use up to 4K tokens of context at Q3_K_S quantization. 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 NVIDIA A100 40GB?

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 NVIDIA A100 40GB See all hardware for Qwen3-Coder-Next

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