Can Qwen3.5 9B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

C43Usable

Estimated from fit model

Qwen3.5 9B needs ~20.8 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~30 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) — 20.8 GB, 29.8 tok/s, Runs well

20.8 GB required108.8 GB available

19% VRAM used

Fit status

Runs well

Decode

29.8 tok/s

TTFT

6489 ms

Safe context

1.4M

Memory

20.8 GB / 108.8 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsQwen3.5 9B on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 29.8 tok/s decode · 6.5s TTFT (warm) · 75 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	C	Runs well	29.8 tok/s	3539 ms	1.4M
Coding	C	Runs well	29.8 tok/s	6489 ms	1.4M
Agentic Coding	C	Runs well	29.8 tok/s	9438 ms	1.4M
Reasoning	C	Runs well	29.8 tok/s	7669 ms	1.4M
RAG	C	Runs well	29.8 tok/s	11798 ms	1.4M

Quantization options

How Qwen3.5 9B (9B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	D39
Q3_K_S	3	4.4 GB	Low	D39
NVFP4	4	5.0 GB	Medium	D39
Q4_K_M	4	5.5 GB	Medium	D39
Q5_K_M	5	6.5 GB	High	D40
Q6_K	6	7.4 GB	High	D40
Q8_0	8	9.6 GB	Very High	D40
F16Best for your GPU	16	18.5 GB	Maximum	C41

Get started

Copy-paste commands to run Qwen3.5 9B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "lmstudio-community/Qwen3.5-9B-GGUF" \
  --hf-file "Qwen3.5-9B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Qwen3.5 9B well

NVIDIA H200 141GBBudget pick

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

Raises estimated decode speed by about 323%.126 tok/s decode

Raises estimated decode speed by about 323%.

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

~$30,000 MSRP

NVIDIA H200 PCIe 141GBBest value

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

Raises estimated decode speed by about 323%.126 tok/s decode

Raises estimated decode speed by about 323%.

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

~$30,000 MSRP

NVIDIA B200 180GBNVIDIA upgrade

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

Raises estimated decode speed by about 323%.126 tok/s decode

Raises estimated decode speed by about 323%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Qwen3.5 9B?

Yes, NVIDIA DGX Spark 128GB can run Qwen3.5 9B with a C grade (Runs well). Expected decode speed: 29.8 tok/s.

How much VRAM does Qwen3.5 9B need?

Qwen3.5 9B (9B parameters) requires approximately 20.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 9B?

The recommended quantization for Qwen3.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 9B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Qwen3.5 9B achieves approximately 29.8 tokens per second decode speed with a time-to-first-token of 6489ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Qwen3.5 9B for coding?

For coding workloads, Qwen3.5 9B on NVIDIA DGX Spark 128GB receives a C grade with 29.8 tok/s and 1.4M context.

What context window can Qwen3.5 9B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Qwen3.5 9B can safely use up to 1.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Qwen3.5 9B?

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 Qwen3.5 9B

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<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--qwen3-5-9b-gguf-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:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

29.8 tok/s

3539 ms

1.4M

Coding

Runs well

29.8 tok/s

6489 ms

1.4M

Agentic Coding

Runs well

29.8 tok/s

9438 ms

1.4M

Reasoning

Runs well

29.8 tok/s

7669 ms

1.4M

RAG

Runs well

29.8 tok/s

11798 ms

1.4M

Quantization options

How Qwen3.5 9B (9B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	D39
Q3_K_S	3	4.4 GB	Low	D39
NVFP4	4	5.0 GB	Medium	D39
Q4_K_M	4	5.5 GB	Medium	D39
Q5_K_M	5	6.5 GB	High	D40
Q6_K	6	7.4 GB	High	D40
Q8_0	8	9.6 GB	Very High	D40
F16Best for your GPU	16	18.5 GB	Maximum	C41

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Qwen3.5 9B?

Yes, NVIDIA DGX Spark 128GB can run Qwen3.5 9B with a C grade (Runs well). Expected decode speed: 29.8 tok/s.

How much VRAM does Qwen3.5 9B need?

Qwen3.5 9B (9B parameters) requires approximately 20.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 9B?

The recommended quantization for Qwen3.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 9B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Qwen3.5 9B achieves approximately 29.8 tokens per second decode speed with a time-to-first-token of 6489ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Qwen3.5 9B for coding?

For coding workloads, Qwen3.5 9B on NVIDIA DGX Spark 128GB receives a C grade with 29.8 tok/s and 1.4M context.

What context window can Qwen3.5 9B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Qwen3.5 9B can safely use up to 1.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Qwen3.5 9B?