Can logos16v2 stablelm2 1.6b i1 run on NVIDIA DGX Spark 128GB?

YES — Runs Great

D40Poor

Estimated from fit model

logos16v2 stablelm2 1.6b i1 needs ~15.4 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M 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.

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 feelslogos16v2 stablelm2 1.6b i1 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	D	Runs well	22.4 tok/s	8643 ms	8.0M
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

Inference speed

logos16v2 stablelm2 1.6b i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for logos16v2 stablelm2 1.6b i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~30 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	30.4	Fits
RTX 4090 24GB	24 GB	Q4_K_M	25.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	25.6	Fits
RTX 3090 24GB	24 GB	Q4_K_M	22.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	22.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	22.4	Fits
RTX 4060 8GB	8 GB	Q4_K_M	22.4	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	22.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	22.4	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	22.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	22.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	22.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	22.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	22.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	22.4	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	22.4	Fits

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 logos16v2 stablelm2 1.6b i1 (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	0.9 GB	Medium	D39
Q4_K_M	4	1.0 GB	Medium	D39
Q5_K_M	5	1.2 GB	High	D39
Q6_K	6	1.3 GB	High	D39
Q8_0	8	1.7 GB	Very High	D39
F16Best for your GPU	16	3.3 GB	Maximum	D39

Get started

Copy-paste commands to run logos16v2 stablelm2 1.6b i1 on your machine.

Run

lms load hf-mradermacher--logos16v2-stablelm2-1-6b-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien logos16v2 stablelm2 1.6b i1

Mac Studio M3 Ultra 256GBOpción económica

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

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.22.4 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$6,999 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run logos16v2 stablelm2 1.6b i1?

Yes, NVIDIA DGX Spark 128GB can run logos16v2 stablelm2 1.6b i1 with a D grade (Runs well). Expected decode speed: 22.4 tok/s.

How much VRAM does logos16v2 stablelm2 1.6b i1 need?

logos16v2 stablelm2 1.6b i1 (1.600000023841858B parameters) requires approximately 15.4 GB of memory with Q4_K_M quantization.

What is the best quantization for logos16v2 stablelm2 1.6b i1?

The recommended quantization for logos16v2 stablelm2 1.6b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will logos16v2 stablelm2 1.6b i1 run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, logos16v2 stablelm2 1.6b i1 achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8643ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run logos16v2 stablelm2 1.6b i1 for coding?

For coding workloads, logos16v2 stablelm2 1.6b i1 on NVIDIA DGX Spark 128GB receives a D grade with 22.4 tok/s and 8.0M context.

What context window can logos16v2 stablelm2 1.6b i1 use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, logos16v2 stablelm2 1.6b i1 can safely use up to 8.0M 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 logos16v2 stablelm2 1.6b i1?

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 logos16v2 stablelm2 1.6b i1

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