Will It Run AI

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
Share:

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

WorkloadGradeFitDecodeTTFTContext
ChatDRuns well22.4 tok/s4714 ms7.5M
CodingDRuns well22.4 tok/s8643 ms8.0M
Agentic CodingCRuns well22.4 tok/s12571 ms8.0M
ReasoningDRuns well22.4 tok/s10214 ms8.0M
RAGCRuns well22.4 tok/s15714 ms8.0M

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).

QuantBitsVRAMQualityFit
Q2_K
2
0.6 GB
LowD39
Q3_K_S
3
0.8 GB
LowD39
NVFP4
4
0.9 GB
MediumD39
Q4_K_M
4
1.0 GB
MediumD39
Q5_K_M
5
1.2 GB
HighD39
Q6_K
6
1.3 GB
HighD39
Q8_0
8
1.7 GB
Very HighD39
F16Best for your GPU
16
3.3 GB
MaximumD39

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

Upgrade-Optionen

Hardware, die logos16v2 stablelm2 1.6b i1 gut ausführt

Mac Studio M3 Ultra 256GBGünstige Wahl
256 GB Unified (+128)819 GB/s (+546)
C
Adds memory headroom for longer context windows and future model growth.22.4 tok/s Dekodierung

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

ca. $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 128GBSee all hardware for logos16v2 stablelm2 1.6b i1
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