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URL: https://willitrunai.com/can-run/hf-mradermacher--logos16v2-stablelm2-1-6b-i1-gguf-on-rtx-5090-32gb

⇱ logos16v2 stablelm2 1.6b i1 on RTX 5090 32GB? YES

Can logos16v2 stablelm2 1.6b i1 run on RTX 5090 32GB?

YES — Runs Great

C41Usable
○Estimated from fit model

logos16v2 stablelm2 1.6b i1 needs ~5.3 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 5.3 GB, 30.4 tok/s, Runs well
5.3 GB required32.0 GB available
17% VRAM used

Fit status

Runs well

Decode

30.4 tok/s

TTFT

6368 ms

Safe context

2.3M

Memory

5.3 GB / 32.0 GB

Memory breakdown

Weights1.0 GB
KV Cache0.2 GB
Runtime0.9 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelslogos16v2 stablelm2 1.6b i1 on RTX 5090 32GB
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: 30.4 tok/s decode · 6.4s TTFT (warm) · 76 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well30.4 tok/s3474 ms2.2M
CodingCRuns well30.4 tok/s6368 ms2.3M
Agentic CodingCRuns well30.4 tok/s9263 ms2.3M
ReasoningCRuns well30.4 tok/s7526 ms2.3M
RAGCRuns well30.4 tok/s11579 ms2.3M

Quantization options

How logos16v2 stablelm2 1.6b i1 (1.600000023841858B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.6 GB
LowC42
Q3_K_S
3
0.8 GB
LowC42
NVFP4
4
0.9 GB
MediumC42
Q4_K_M
4
1.0 GB
MediumC42
Q5_K_M
5
1.2 GB
HighC42
Q6_K
6
1.3 GB
HighC42
Q8_0
8
1.7 GB
Very HighC42
F16Best for your GPU
16
3.3 GB
MaximumC43

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

Frequently asked questions

See all results for RTX 5090 32GBSee all hardware for logos16v2 stablelm2 1.6b i1