Can Qwen 3.5 35B A3B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

S86Excellent

Estimated from fit model

Qwen 3.5 35B A3B needs ~37.1 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced

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) — 37.1 GB, 22.6 tok/s, Runs well

37.1 GB required108.8 GB available

34% VRAM used

Fit status

Runs well

Decode

22.6 tok/s

TTFT

8552 ms

Safe context

131K

Memory

37.1 GB / 108.8 GB

Memory breakdown

Weights21.3 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsQwen 3.5 35B A3B 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: 22.6 tok/s decode · 8.6s TTFT (warm) · 57 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	22.6 tok/s	4665 ms	131K
Coding	S	Runs well	22.6 tok/s	8552 ms	131K
Agentic Coding	S	Runs well	22.6 tok/s	12440 ms	131K
Reasoning	S	Runs well	22.6 tok/s	10107 ms	131K
RAG	S	Runs well	22.6 tok/s	15550 ms	131K

Inference speed

Qwen 3.5 35B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.5 35B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~139 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?
RTX 5090 32GB	32 GB	Q4_K_M	139.4	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	100.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	83.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	79.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	71.1	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	61.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	61.8	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	60.0	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	56.9	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	41.5	Tight
RTX 4080 Super 16GB	16 GB	Q4_K_M	25.2	Too big
RTX 4070 12GB	12 GB	Q4_K_M	8.8	Too big
RTX 3060 12GB	12 GB	Q4_K_M	5.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.4	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 35B A3B (35B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A82
Q3_K_S	3	17.2 GB	Low	A82
NVFP4	4	19.6 GB	Medium	A82
Q4_K_M	4	21.3 GB	Medium	A83
Q5_K_M	5	25.2 GB	High	A83
Q6_K	6	28.7 GB	High	A84
Q8_0	8	37.5 GB	Very High	S86
F16Best for your GPU	16	71.8 GB	Maximum	S89

Get started

Copy-paste commands to run Qwen 3.5 35B A3B on your machine.

Run

ollama run qwen3.5:35b-a3b

Your hardware

More models your NVIDIA DGX Spark 128GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	2.4 tok/s
Qwen 3.5 122B A10B	122B	S	6.6 tok/s

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Qwen 3.5 35B A3B?

Yes, NVIDIA DGX Spark 128GB can run Qwen 3.5 35B A3B with a S grade (Runs well). Expected decode speed: 22.6 tok/s.

How much VRAM does Qwen 3.5 35B A3B need?

Qwen 3.5 35B A3B (35B parameters) requires approximately 37.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 35B A3B?

The recommended quantization for Qwen 3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 35B A3B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Qwen 3.5 35B A3B achieves approximately 22.6 tokens per second decode speed with a time-to-first-token of 8552ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Qwen 3.5 35B A3B for coding?

For coding workloads, Qwen 3.5 35B A3B on NVIDIA DGX Spark 128GB receives a S grade with 22.6 tok/s and 131K context.

What context window can Qwen 3.5 35B A3B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Qwen 3.5 35B A3B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Qwen 3.5 35B A3B?

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 35B A3B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/qwen-3.5-35b-a3b-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: