Can logos16v2 stablelm2 1.6b i1 run on RTX 4090 Laptop 16GB?

YES — Runs Great

C42Usable

Estimated from fit model

logos16v2 stablelm2 1.6b i1 needs ~3.7 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 3.7 GB, 25.6 tok/s, Runs well

3.7 GB required16.0 GB available

23% VRAM used

Fit status

Runs well

Decode

25.6 tok/s

TTFT

7562 ms

Safe context

1.1M

Memory

3.7 GB / 16.0 GB

Memory breakdown

Weights1.0 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelslogos16v2 stablelm2 1.6b i1 on RTX 4090 Laptop 16GB
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: 25.6 tok/s decode · 7.6s TTFT (warm) · 64 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	25.6 tok/s	4125 ms	1.0M
Coding	C	Runs well	25.6 tok/s	7562 ms	1.1M
Agentic Coding	C	Runs well	25.6 tok/s	11000 ms	1.1M
Reasoning	C	Runs well	25.6 tok/s	8937 ms	1.1M
RAG	C	Runs well	25.6 tok/s	13750 ms	1.1M

Quantization options

How logos16v2 stablelm2 1.6b i1 (1.600000023841858B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C45
Q3_K_S	3	0.8 GB	Low	C45
NVFP4	4	0.9 GB	Medium	C45
Q4_K_M	4	1.0 GB	Medium	C45
Q5_K_M	5	1.2 GB	High	C45
Q6_K	6	1.3 GB	High	C45
Q8_0	8	1.7 GB	Very High	C46
F16Best for your GPU	16	3.3 GB	Maximum	C47

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

升级选项

能流畅运行 logos16v2 stablelm2 1.6b i1 的硬件

MacBook Pro M3 24GB经济之选

24 GB Unified (+8)

This setup is broadly balanced for this model.22.4 tok/s 解码

~$1,099 MSRP

MacBook Air M3 24GB最佳性价比

24 GB Unified (+8)

This setup is broadly balanced for this model.22.4 tok/s 解码

~$1,099 MSRP

Frequently asked questions

Can RTX 4090 Laptop 16GB run logos16v2 stablelm2 1.6b i1?

Yes, RTX 4090 Laptop 16GB can run logos16v2 stablelm2 1.6b i1 with a C grade (Runs well). Expected decode speed: 25.6 tok/s.

How much VRAM does logos16v2 stablelm2 1.6b i1 need?

logos16v2 stablelm2 1.6b i1 (1.600000023841858B parameters) requires approximately 3.7 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 RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, logos16v2 stablelm2 1.6b i1 achieves approximately 25.6 tokens per second decode speed with a time-to-first-token of 7562ms using Q4_K_M quantization.

Can RTX 4090 Laptop 16GB run logos16v2 stablelm2 1.6b i1 for coding?

For coding workloads, logos16v2 stablelm2 1.6b i1 on RTX 4090 Laptop 16GB receives a C grade with 25.6 tok/s and 1.1M context.

What context window can logos16v2 stablelm2 1.6b i1 use on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, logos16v2 stablelm2 1.6b i1 can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4090 Laptop 16GB See all hardware for logos16v2 stablelm2 1.6b i1

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