Can Qwen3-Coder-Next run on Tesla P100 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen3-Coder-Next needs ~53.1 GB but Tesla P100 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: 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

4.4 tok/s

TTFT

44254 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 Tesla P100 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: 4.4 tok/s decode · 44.3s TTFT (warm) · 11 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	4.4 tok/s	24139 ms	4K
Coding	F	Too heavy	4.4 tok/s	44254 ms	4K
Agentic Coding	F	Too heavy	4.4 tok/s	64370 ms	4K
Reasoning	F	Too heavy	4.4 tok/s	52301 ms	4K
RAG	F	Too heavy	4.4 tok/s	80462 ms	4K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on Tesla P100 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	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

Opciones de mejora

Hardware que ejecuta bien Qwen3-Coder-Next

RTX A6000 48GBMejor relación calidad-precio

48 GB VRAM (+32)768 GB/s (+36)

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 382%.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBMejora NVIDIA

48 GB VRAM (+32)1344 GB/s (+612)

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 859%.

~$4,999 MSRP

NVIDIA A16 64GBOpción económica

64 GB VRAM (+48)

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 (+64)2039 GB/s (+1307)

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 Tesla P100 16GB run Qwen3-Coder-Next?

No, Qwen3-Coder-Next requires more memory than Tesla P100 16GB provides.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 53.1 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 Tesla P100 16GB?

On Tesla P100 16GB, Qwen3-Coder-Next achieves approximately 4.4 tokens per second decode speed with a time-to-first-token of 44254ms using Q4_K_M quantization.

Can Tesla P100 16GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on Tesla P100 16GB receives a F grade with 4.4 tok/s and 4K context.

What context window can Qwen3-Coder-Next use on Tesla P100 16GB?

On Tesla P100 16GB, Qwen3-Coder-Next can safely use up to 4K tokens of context. 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 Tesla P100 16GB?

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.

See all results for Tesla P100 16GB See all hardware for Qwen3-Coder-Next

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