Can stablelm 2 zephyr 1.6b run on RTX 5060 Ti 8GB?

YES — Runs Great

C46Usable

○Estimated from fit model

stablelm 2 zephyr 1.6b needs ~2.9 GB VRAM. RTX 5060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 2.9 GB, 30.4 tok/s, Runs well

2.9 GB required8.0 GB available

36% VRAM used

Fit status

Runs well

Decode

30.4 tok/s

TTFT

6368 ms

Safe context

454K

Memory

2.9 GB / 8.0 GB

Memory breakdown

Weights1.0 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsstablelm 2 zephyr 1.6b on RTX 5060 Ti 8GB
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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	30.4 tok/s	3474 ms	426K
Coding	C	Runs well	30.4 tok/s	6368 ms	454K
Agentic Coding	C	Runs well	30.4 tok/s	9263 ms	454K
Reasoning	C	Runs well	30.4 tok/s	7526 ms	454K
RAG	C	Runs well	30.4 tok/s	11579 ms	454K

Quantization options

How stablelm 2 zephyr 1.6b (1.600000023841858B params) fits at each quantization level on RTX 5060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C49
Q3_K_S	3	0.8 GB	Low	C49
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

Frequently asked questions

See all results for RTX 5060 Ti 8GB 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-rtx-5060-ti-8gb

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