Can stablelm 2 zephyr 1.6b run on NVIDIA DGX Spark 128GB?

YES — With F16

C40Usable

○Estimated from fit model

stablelm 2 zephyr 1.6b needs ~17.7 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~22 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.

stablelm 2 zephyr 1.6b at Q4_K_M needs 2.4 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (17.7 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 15.4 GB, 22.4 tok/s, Runs well

15.4 GB required108.8 GB available

14% VRAM used

Fit status

Runs well

Decode

22.4 tok/s

TTFT

8643 ms

Safe context

8.0M

Memory

15.4 GB / 108.8 GB

Memory breakdown

Weights1.0 GB

KV Cache0.2 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsstablelm 2 zephyr 1.6b 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: 22.4 tok/s decode · 8.6s TTFT (warm) · 56 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	D	Runs well	22.4 tok/s	4714 ms	7.5M
Coding	F	Too heavy	22.4 tok/s	8643 ms	4K
Agentic Coding	C	Runs well	22.4 tok/s	12571 ms	8.0M
Reasoning	D	Runs well	22.4 tok/s	10214 ms	8.0M
RAG	C	Runs well	22.4 tok/s	15714 ms	8.0M

Quantization options

How stablelm 2 zephyr 1.6b (1.600000023841858B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	D39
Q3_K_S	3	0.8 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run stablelm 2 zephyr 1.6b on your machine.

Run

lms load hf-second-state--stablelm-2-zephyr-1-6b-gguf && lms server start

Upgrade options

Hardware that runs stablelm 2 zephyr 1.6b well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)819 GB/s (+546)

Adds memory headroom for longer context windows and future model growth.22.4 tok/s decode

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

~$6,999 MSRP

Frequently asked questions

See all results for NVIDIA DGX Spark 128GB See all hardware for stablelm 2 zephyr 1.6b

URL: https://willitrunai.com/can-run/hf-second-state--stablelm-2-zephyr-1-6b-gguf-on-dgx-spark-128gb

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