Can Apertus v1.5 8B run on RTX 3050 Ti Laptop 4GB?

Q: What should I upgrade first if Apertus v1.5 8B feels slow on RTX 3050 Ti Laptop 4GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q1_0_G128

B67Good

Estimated from fit model

Apertus v1.5 8B needs ~4.5 GB VRAM. RTX 3050 Ti Laptop 4GB has 4.0 GB. With Q1_0_G128 quantization, expect ~64 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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.

Apertus v1.5 8B at Q4_K_M needs 8.7 GB — too much for RTX 3050 Ti Laptop 4GB (4.0 GB). Runs at Q1_0_G128 (4.5 GB) with very low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.7 GB, exceeds 4.0 GB available

8.7 GB required4.0 GB available

217% VRAM needed

4.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.7 tok/s

TTFT

51814 ms

Safe context

Memory

8.7 GB / 4.0 GB

Offload

50%

Memory breakdown

Weights5.4 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsApertus v1.5 8B on RTX 3050 Ti Laptop 4GB
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: 3.7 tok/s decode · 51.8s TTFT (warm) · 9 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	5.2 tok/s	20291 ms	4K
Coding	F	Too heavy	4.1 tok/s	46788 ms	4K
Agentic Coding	F	Too heavy	4.1 tok/s	68056 ms	4K
Reasoning	F	Too heavy	4.1 tok/s	55295 ms	4K
RAG	F	Too heavy	4.1 tok/s	85070 ms	4K

Inference speed

Apertus v1.5 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Apertus v1.5 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~169 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	169.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	139.6	Fits

Quantization options

How Apertus v1.5 8B (8.899999618530273B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128Best for your GPU	1.125	1.3 GB	Very Low	A80
Q2_0_G128	1.71	2.4 GB	Low	F0

Get started

Copy-paste commands to run Apertus v1.5 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "swiss-ai/Apertus-v1.5-8B" \
  --hf-file "Apertus-v1.5-8B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Apertus v1.5 8B well

RTX 3050 8GBBest value

8 GB VRAM (+4)224 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.14.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 281%.

~$249 MSRP

RTX 5060 8GBNVIDIA upgrade

8 GB VRAM (+4)448 GB/s (+256)

Makes the model fit on the accelerator instead of staying completely out of reach.31.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 762%.

~$299 MSRP

RTX 3060 12GBBudget pick

12 GB VRAM (+8)360 GB/s (+168)

Makes the model fit on the accelerator instead of staying completely out of reach.39.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$329 MSRP

Frequently asked questions

Can RTX 3050 Ti Laptop 4GB run Apertus v1.5 8B?

Yes, RTX 3050 Ti Laptop 4GB can run Apertus v1.5 8B at Q1_0_G128 quantization (Very compromised (needs ~0.2 GB host RAM)). The recommended Q4_K_M requires 8.7 GB which exceeds available memory, but at Q1_0_G128 it needs only 4.5 GB. Expected decode speed: 63.8 tok/s.

How much VRAM does Apertus v1.5 8B need?

Apertus v1.5 8B (8.899999618530273B parameters) requires approximately 8.7 GB at Q4_K_M quantization. On RTX 3050 Ti Laptop 4GB, it fits at Q1_0_G128 using 4.5 GB.

What is the best quantization for Apertus v1.5 8B?

The recommended quantization is Q4_K_M, but on RTX 3050 Ti Laptop 4GB the best fitting quantization is Q1_0_G128, which uses 4.5 GB.

What speed will Apertus v1.5 8B run at on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, Apertus v1.5 8B achieves approximately 63.8 tokens per second decode speed with a time-to-first-token of 3034ms using Q1_0_G128 quantization.

Can RTX 3050 Ti Laptop 4GB run Apertus v1.5 8B for coding?

For coding workloads, Apertus v1.5 8B on RTX 3050 Ti Laptop 4GB receives a F grade with 4.1 tok/s and 4K context.

What context window can Apertus v1.5 8B use on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, Apertus v1.5 8B can safely use up to 12K tokens of context at Q1_0_G128 quantization. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for RTX 3050 Ti Laptop 4GB See all hardware for Apertus v1.5 8B

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