Can Ministral 3 3B Instruct 2512 run on RTX 5090 Laptop 24GB?

YES — Runs Great

C45Usable

Estimated from fit model

Ministral 3 3B Instruct 2512 needs ~5.8 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 5.8 GB, 42.0 tok/s, Runs well

5.8 GB required24.0 GB available

24% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

845K

Memory

5.8 GB / 24.0 GB

Memory breakdown

Weights1.8 GB

KV Cache0.4 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsMinistral 3 3B Instruct 2512 on RTX 5090 Laptop 24GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	42.0 tok/s	2514 ms	845K
Coding	C	Runs well	42.0 tok/s	4610 ms	845K
Agentic Coding	C	Runs well	42.0 tok/s	6705 ms	845K
Reasoning	C	Runs well	42.0 tok/s	5448 ms	845K
RAG	C	Runs well	42.0 tok/s	8381 ms	845K

Quantization options

How Ministral 3 3B Instruct 2512 (3B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	C44
Q3_K_S	3	1.5 GB	Low	C44
NVFP4	4

Get started

Copy-paste commands to run Ministral 3 3B Instruct 2512 on your machine.

Run

lms load hf-mistralai--ministral-3-3b-instruct-2512-gguf && lms server start

Upgrade options

Hardware that runs Ministral 3 3B Instruct 2512 well

MacBook Pro M3 Pro 36GBBudget pick

36 GB Unified (+12)

This setup is broadly balanced for this model.42 tok/s decode

~$1,999 MSRP

MacBook Pro M4 Max 36GBBest value

36 GB Unified (+12)

This setup is broadly balanced for this model.42 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can RTX 5090 Laptop 24GB run Ministral 3 3B Instruct 2512?

Yes, RTX 5090 Laptop 24GB can run Ministral 3 3B Instruct 2512 with a C grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does Ministral 3 3B Instruct 2512 need?

Ministral 3 3B Instruct 2512 (3B parameters) requires approximately 5.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 3 3B Instruct 2512?

The recommended quantization for Ministral 3 3B Instruct 2512 is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 3 3B Instruct 2512 run at on RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, Ministral 3 3B Instruct 2512 achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can RTX 5090 Laptop 24GB run Ministral 3 3B Instruct 2512 for coding?

For coding workloads, Ministral 3 3B Instruct 2512 on RTX 5090 Laptop 24GB receives a C grade with 42.0 tok/s and 845K context.

What context window can Ministral 3 3B Instruct 2512 use on RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, Ministral 3 3B Instruct 2512 can safely use up to 845K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5090 Laptop 24GB See all hardware for Ministral 3 3B Instruct 2512

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