Can Qwen 3.6 35B A3B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

S87Excellent

Estimated from fit model

Qwen 3.6 35B A3B needs ~41.1 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: SGLangCapacity: RoomyBandwidth: LowStack: OptimizedBottleneck: 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) — 41.1 GB, 20.8 tok/s, Runs well

41.1 GB required108.8 GB available

38% VRAM used

Fit status

Runs well

Decode

20.8 tok/s

TTFT

9301 ms

Safe context

262K

Memory

41.1 GB / 108.8 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime2.6 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsQwen 3.6 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: 20.8 tok/s decode · 9.3s TTFT (warm) · 52 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	20.8 tok/s	5073 ms	262K
Coding	S	Runs well	20.8 tok/s	9301 ms	262K
Agentic Coding	F	Too heavy	3.7 tok/s	75156 ms	4K
Reasoning	S	Runs well	20.8 tok/s	10992 ms	262K
RAG	S	Runs well	20.8 tok/s	16910 ms	262K

Quantization options

How Qwen 3.6 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	A83
NVFP4	4	19.6 GB	Medium	A83
Q4_K_M	4	21.3 GB	Medium	A83
Q5_K_M	5	25.2 GB	High	A84
Q6_K	6	28.7 GB	High	A85
Q8_0	8	37.5 GB	Very High	S87
F16Best for your GPU	16	71.8 GB	Maximum	S90

Get started

Copy-paste commands to run Qwen 3.6 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "Qwen/Qwen3.6-35B-A3B" \
  --hf-file "Qwen3.6-35B-A3B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

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 3.6 35B A3B?

Yes, NVIDIA DGX Spark 128GB can run Qwen 3.6 35B A3B with a S grade (Runs well). Expected decode speed: 20.8 tok/s.

How much VRAM does Qwen 3.6 35B A3B need?

Qwen 3.6 35B A3B (35B parameters) requires approximately 41.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.6 35B A3B?

The recommended quantization for Qwen 3.6 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.6 35B A3B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Qwen 3.6 35B A3B achieves approximately 20.8 tokens per second decode speed with a time-to-first-token of 9301ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Qwen 3.6 35B A3B for coding?

For coding workloads, Qwen 3.6 35B A3B on NVIDIA DGX Spark 128GB receives a S grade with 20.8 tok/s and 262K context.

What context window can Qwen 3.6 35B A3B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Qwen 3.6 35B A3B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Qwen 3.6 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 3.6 35B A3B

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