Can Qwen3-Coder-Next run on NVIDIA V100 32GB?

YES — With Q2_K

A81Great

Estimated from fit model

Qwen3-Coder-Next needs ~37.1 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q2_K quantization, expect ~36 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 54.7 GB — too much for NVIDIA V100 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

14.4 tok/s

TTFT

13486 ms

Safe context

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 NVIDIA V100 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: 14.4 tok/s decode · 13.5s TTFT (warm) · 36 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	14.7 tok/s	7194 ms	4K
Coding	F	Too heavy	14.4 tok/s	13486 ms	4K
Agentic Coding	F	Too heavy	13.7 tok/s	20491 ms	4K
Reasoning	F	Too heavy	14.4 tok/s	15938 ms	4K
RAG	F	Too heavy	13.7 tok/s	25614 ms	4K

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 V100 32GB (32.0 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 (+16)

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.

Sube la velocidad estimada de decodificación alrededor de un 47%.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBMejora NVIDIA

48 GB VRAM (+16)1344 GB/s (+444)

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 193%.

~$4,999 MSRP

NVIDIA A16 64GBOpción económica

64 GB VRAM (+32)

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 (+48)2039 GB/s (+1139)

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 V100 32GB run Qwen3-Coder-Next?

Yes, NVIDIA V100 32GB can run Qwen3-Coder-Next at Q2_K quantization (Very compromised (needs ~4.3 GB host RAM)). The recommended Q4_K_M requires 54.7 GB which exceeds available memory, but at Q2_K it needs only 37.1 GB. Expected decode speed: 36.2 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 54.7 GB at Q4_K_M quantization. On NVIDIA V100 32GB, it fits at Q2_K using 37.1 GB.

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

The recommended quantization is Q4_K_M, but on NVIDIA V100 32GB the best fitting quantization is Q2_K, which uses 37.1 GB.

What speed will Qwen3-Coder-Next run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Qwen3-Coder-Next achieves approximately 36.2 tokens per second decode speed with a time-to-first-token of 5343ms using Q2_K quantization.

Can NVIDIA V100 32GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on NVIDIA V100 32GB receives a F grade with 14.4 tok/s and 4K context.

What context window can Qwen3-Coder-Next use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Qwen3-Coder-Next can safely use up to 4K tokens of context at Q2_K 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 V100 32GB?

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 V100 32GB See all hardware for Qwen3-Coder-Next

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