Can Qwen 3.5 122B A10B run on NVIDIA DGX Spark 128GB?

YES — With Q6_K

A77Great

Estimated from fit model

Qwen 3.5 122B A10B needs ~116.4 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With Q6_K quantization, expect ~4 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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.

Qwen 3.5 122B A10B at Q4_K_M needs 77.8 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at Q6_K (116.4 GB) with high quality. 6 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 90.8 GB, 6.6 tok/s, Tight fit

90.8 GB required108.8 GB available

83% VRAM used

Fit status

Tight fit

Decode

6.6 tok/s

TTFT

29399 ms

Safe context

131K

Memory

90.8 GB / 108.8 GB

Memory breakdown

Weights74.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsQwen 3.5 122B A10B on NVIDIA DGX Spark 128GB
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: 6.6 tok/s decode · 29.4s TTFT (warm) · 17 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.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

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 6.6 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	S	Tight fit	6.6 tok/s	16036 ms	131K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	S	Tight fit	6.6 tok/s	42763 ms	131K
Reasoning	S	Tight fit	6.6 tok/s	34745 ms	131K
RAG	S	Tight fit	6.6 tok/s	53453 ms	131K

Quantization options

How Qwen 3.5 122B A10B (122B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	S90
Q3_K_S	3	59.8 GB	Low	S90
NVFP4	4	68.3 GB	Medium	S90
Q4_K_MBest for your GPU	4	74.4 GB	Medium	S90
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

Get started

Copy-paste commands to run Qwen 3.5 122B A10B on your machine.

Run

lms load Qwen3.5-122B-A10B-Instruct && lms server start

Upgrade-Optionen

Hardware, die Qwen 3.5 122B A10B gut ausführt

NVIDIA H200 141GBGünstige Wahl

141 GB VRAM (+13)4800 GB/s (+4527)

Raises estimated decode speed by about 2356%.162.1 tok/s Dekodierung

Raises estimated decode speed by about 2356%.

Adds memory headroom for longer context windows and future model growth.

ca. $30,000 MSRP

NVIDIA H200 PCIe 141GBBestes Preis-Leistungs-Verhältnis

141 GB VRAM (+13)4800 GB/s (+4527)

Raises estimated decode speed by about 2356%.162.1 tok/s Dekodierung

Raises estimated decode speed by about 2356%.

Adds memory headroom for longer context windows and future model growth.

ca. $30,000 MSRP

NVIDIA B200 180GBNVIDIA-Upgrade

180 GB VRAM (+52)8000 GB/s (+7727)

Raises estimated decode speed by about 3994%.270.2 tok/s Dekodierung

Raises estimated decode speed by about 3994%.

Adds memory headroom for longer context windows and future model growth.

ca. $30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Qwen 3.5 122B A10B?

Yes, NVIDIA DGX Spark 128GB can run Qwen 3.5 122B A10B at Q6_K quantization (Runs with offload (needs ~6.6 GB host RAM)). The recommended Q4_K_M requires 77.8 GB which exceeds available memory, but at Q6_K it needs only 116.4 GB. Expected decode speed: 4.2 tok/s.

How much VRAM does Qwen 3.5 122B A10B need?

Qwen 3.5 122B A10B (122B parameters) requires approximately 77.8 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at Q6_K using 116.4 GB.

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

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is Q6_K, which uses 116.4 GB.

What speed will Qwen 3.5 122B A10B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Qwen 3.5 122B A10B achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 46311ms using Q6_K quantization.

Can NVIDIA DGX Spark 128GB run Qwen 3.5 122B A10B for coding?

For coding workloads, Qwen 3.5 122B A10B on NVIDIA DGX Spark 128GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Qwen 3.5 122B A10B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Qwen 3.5 122B A10B can safely use up to 4K tokens of context at Q6_K quantization. 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 DGX Spark 128GB?

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.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Qwen 3.5 122B A10B?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GB See all hardware for Qwen 3.5 122B A10B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/qwen-3.5-122b-a10b-on-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: