6 GB VRAM (+2)336 GB/s (+144)
B
Raises estimated decode speed by about 61%.56 tok/s decode
Raises estimated decode speed by about 61%.
Adds memory headroom for longer context windows and future model growth.
~$229 MSRP
Can gemma 3 4b it run on RTX 3050 Ti Laptop 4GB?
YES — With Offload
C51Usable
○Estimated from fit model
gemma 3 4b it needs ~4.2 GB VRAM. RTX 3050 Ti Laptop 4GB has 4.0 GB. With Q4_K_M quantization, expect ~35 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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.
Select quantization to explore
Q4_K_M (Medium quality) — 4.2 GB, 34.7 tok/s, Runs with offload (needs ~0.1 GB host RAM)
4.2 GB required4.0 GB available
105% VRAM needed
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
34.7 tok/s
TTFT
5573 ms
Safe context
9K
Memory
4.2 GB / 4.0 GB
Memory breakdown
Weights2.4 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom0.4 GB
See how fast it feels
See how fast it feelsgemma 3 4b it 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: 34.7 tok/s decode · 5.6s TTFT (warm) · 87 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 48.0 tok/s | 2200 ms | 9K |
| Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 34.7 tok/s | 5573 ms | 9K |
| Agentic Coding | C | Very compromised (needs ~0.4 GB host RAM) | 27.8 tok/s | 10123 ms | 9K |
| Reasoning | C | Runs with offload (needs ~0.1 GB host RAM) | 34.7 tok/s | 6586 ms | 9K |
| RAG | C | Very compromised (needs ~0.4 GB host RAM) | 27.8 tok/s |
Quantization options
How gemma 3 4b it (4B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 1.6 GB | Low | B55 |
Q3_K_S | 3 | 2.0 GB | Low | F0 |
Get started
Copy-paste commands to run gemma 3 4b it on your machine.
Run
lms load hf-lmstudio-community--gemma-3-4b-it-gguf && lms server startUpgrade options
Hardware that runs gemma 3 4b it well
6 GB VRAM (+2)
C
Raises estimated decode speed by about 34%.46.4 tok/s decode
Raises estimated decode speed by about 34%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
8 GB VRAM (+4)224 GB/s (+32)
C
Raises estimated decode speed by about 38%.48 tok/s decode
Raises estimated decode speed by about 38%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP