Ministral 3 3B needs ~4.8 GB VRAM. RTX 3050 Ti Laptop 4GB has 4.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Operating mode
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
0.8 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.3 GB host RAM)
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
4K
Memory
4.8 GB / 4.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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.
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 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Very compromised (needs ~0.2 GB host RAM) | 42.0 tok/s | 2514 ms | 4K |
| Coding | B | Very compromised (needs ~0.3 GB host RAM) | 42.0 tok/s | 4610 ms | 4K |
| Agentic Coding | F | Too heavy | 33.8 tok/s | 8327 ms | 4K |
| Reasoning | B | Very compromised (needs ~0.3 GB host RAM) | 42.0 tok/s | 5448 ms | 4K |
| RAG | F | Too heavy | 33.8 tok/s | 10409 ms | 4K |
How Ministral 3 3B (3B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | A78 |
Q3_K_S | 3 | 1.5 GB | Low | A78 |
NVFP4 | 4 | 1.7 GB | Medium | A77 |
Q4_K_MBest for your GPU | 4 | 1.8 GB | Medium | A77 |
Q5_K_M | 5 | 2.2 GB | High | F0 |
Q6_K | 6 | 2.5 GB | High | F0 |
Q8_0 | 8 | 3.2 GB | Very High | F0 |
F16 | 16 | 6.1 GB | Maximum | F0 |
Copy-paste commands to run Ministral 3 3B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "mistralai/Ministral-3-3B-Instruct-2512" \
--hf-file "Ministral-3-3B-Instruct-2512-Q4_K_M.gguf" \
-c 4096 -ngl 99Opções de upgrade
GTX 1660 Super 6GBOpção econômicaRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$229 MSRP
RTX 3050 8GBMelhor custo-benefícioRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
GTX 1060 6GBUpgrade NVIDIARemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
Yes, RTX 3050 Ti Laptop 4GB can run Ministral 3 3B with a B grade (Very compromised (needs ~0.3 GB host RAM)). Expected decode speed: 42.0 tok/s.
Ministral 3 3B (3B parameters) requires approximately 4.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Ministral 3 3B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, Ministral 3 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
For coding workloads, Ministral 3 3B on RTX 3050 Ti Laptop 4GB receives a B grade with 42.0 tok/s and 4K context.
On RTX 3050 Ti Laptop 4GB, Ministral 3 3B can safely use up to 4K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/ministral-3-3b-on-rtx-3050-ti-laptop-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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