Can Qwen 3.5 122B A10B run on NVIDIA A30 24GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen 3.5 122B A10B needs ~80.2 GB but NVIDIA A30 24GB only has 24.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: 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) — 80.2 GB, exceeds 24.0 GB available

80.2 GB required24.0 GB available

334% VRAM needed

56.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.4 tok/s

TTFT

44115 ms

Safe context

Memory

80.2 GB / 24.0 GB

Offload

70%

Memory breakdown

Weights74.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3.5 122B A10B on NVIDIA A30 24GB
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 · 44.1s 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 80.2 GB, but this setup only exposes 24.0 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	4.4 tok/s	24063 ms	4K
Coding	F	Too heavy	4.4 tok/s	44115 ms	4K
Agentic Coding	F	Too heavy	4.4 tok/s	64167 ms	4K
Reasoning	F	Too heavy	4.4 tok/s	52136 ms	4K
RAG	F	Too heavy	4.4 tok/s	80209 ms	4K

Inference speed

Qwen 3.5 122B A10B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.5 122B A10B 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 ~35 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	34.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	28.9	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	27.4	Offloads
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	21.4	Offloads
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	11.3	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	10.0	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	7.6	Too big
RTX 5090 32GB	32 GB	Q4_K_M	7.2	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	7.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	6.5	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	6.4	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	5.9	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	5.7	Too big
RTX 4090 24GB	24 GB	Q4_K_M	4.6	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	4.2	Too big
RTX 3090 24GB	24 GB	Q4_K_M	4.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	3.7	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.3	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	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 Qwen 3.5 122B A10B (122B params) fits at each quantization level on NVIDIA A30 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	F0
Q3_K_S	3	59.8 GB	Low	F0
NVFP4	4	68.3 GB	Medium	F0
Q4_K_M	4	74.4 GB	Medium	F0
Q5_K_M	5	87.8 GB	High	F0
Q6_K	6	100.0 GB	High	F0
Q8_0	8	130.5 GB	Very High	F0
F16	16	250.1 GB	Maximum	F0

Opciones de mejora

Hardware que ejecuta bien Qwen 3.5 122B A10B

RTX PRO 6000 Blackwell Workstation Edition 96GBOpción económica

96 GB VRAM (+72)1792 GB/s (+859)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.60.5 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.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBMejor relación calidad-precio

96 GB VRAM (+72)1597 GB/s (+664)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.53.9 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.

~$9,999 MSRP

NVIDIA H20 96GBMejora NVIDIA

96 GB VRAM (+72)4000 GB/s (+3067)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.130.3 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.

~$12,000 MSRP

Frequently asked questions

Can NVIDIA A30 24GB run Qwen 3.5 122B A10B?

No, Qwen 3.5 122B A10B requires more memory than NVIDIA A30 24GB provides.

How much VRAM does Qwen 3.5 122B A10B need?

Qwen 3.5 122B A10B (122B parameters) requires approximately 80.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 122B A10B?

The recommended quantization for Qwen 3.5 122B A10B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 122B A10B run at on NVIDIA A30 24GB?

On NVIDIA A30 24GB, Qwen 3.5 122B A10B achieves approximately 4.4 tokens per second decode speed with a time-to-first-token of 44115ms using Q4_K_M quantization.

Can NVIDIA A30 24GB run Qwen 3.5 122B A10B for coding?

For coding workloads, Qwen 3.5 122B A10B on NVIDIA A30 24GB receives a F grade with 4.4 tok/s and 4K context.

What context window can Qwen 3.5 122B A10B use on NVIDIA A30 24GB?

On NVIDIA A30 24GB, Qwen 3.5 122B A10B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.5 122B A10B feels slow on NVIDIA A30 24GB?

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 NVIDIA A30 24GB See all hardware for Qwen 3.5 122B A10B

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