Will It Run AI

Can Ministral 8B run on RTX 3000 Ada Laptop 8GB?

BARELY — Tight on Memory

C49Usable
Estimated from fit model

Ministral 8B needs ~9.1 GB VRAM. RTX 3000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload
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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) — 9.1 GB, 26.6 tok/s, Very compromised (needs ~0.6 GB host RAM)
9.1 GB required8.0 GB available
114% VRAM needed

1.1 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.6 GB host RAM)

Decode

26.6 tok/s

TTFT

7272 ms

Safe context

8K

Memory

9.1 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB
KV Cache2.2 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsMinistral 8B on RTX 3000 Ada Laptop 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: 26.6 tok/s decode · 7.3s TTFT (warm) · 67 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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns with offload46.3 tok/s2280 ms8K
CodingCVery compromised24.8 tok/s7817 ms8K
Agentic CodingFToo heavy16.9 tok/s16693 ms8K
ReasoningCVery compromised (needs ~0.6 GB host RAM)26.6 tok/s8594 ms8K
RAGFToo heavy16.9 tok/s20866 ms8K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on RTX 3000 Ada Laptop 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowB63
Q3_K_S
3
3.9 GB
LowB63
NVFP4
4
4.5 GB
MediumB63
Q4_K_MBest for your GPU
4
4.9 GB
MediumB62
Q5_K_M
5
5.8 GB
HighF0
Q6_K
6
6.6 GB
HighF0
Q8_0
8
8.6 GB
Very HighF0
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run Ministral 8B on your machine.

Run

ollama run ministral

升级选项

能流畅运行 Ministral 8B 的硬件

NVIDIARTX 3060 12GB经济之选
12 GB VRAM (+4)360 GB/s (+72)
B
Removes host-memory offload, which is usually the single biggest latency and throughput win.52.3 tok/s 解码

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

Raises estimated decode speed by about 97%.

~$329 MSRP

NVIDIARTX 5060 Ti 16GB最佳性价比
16 GB VRAM (+8)448 GB/s (+160)
B
Removes host-memory offload, which is usually the single biggest latency and throughput win.61.2 tok/s 解码

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

Raises estimated decode speed by about 130%.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBNVIDIA 升级
16 GB VRAM (+8)
B
Removes host-memory offload, which is usually the single biggest latency and throughput win.46.3 tok/s 解码

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

Raises estimated decode speed by about 74%.

~$499 MSRP

Frequently asked questions

Can RTX 3000 Ada Laptop 8GB run Ministral 8B?

Yes, RTX 3000 Ada Laptop 8GB can run Ministral 8B with a C grade (Very compromised). Expected decode speed: 24.8 tok/s.

How much VRAM does Ministral 8B need?

Ministral 8B (8B parameters) requires approximately 9.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 8B?

The recommended quantization for Ministral 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 8B run at on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, Ministral 8B achieves approximately 24.8 tokens per second decode speed with a time-to-first-token of 7817ms using Q4_K_M quantization.

Can RTX 3000 Ada Laptop 8GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on RTX 3000 Ada Laptop 8GB receives a C grade with 24.8 tok/s and 8K context.

What context window can Ministral 8B use on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, Ministral 8B can safely use up to 8K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Ministral 8B feels slow on RTX 3000 Ada Laptop 8GB?

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.

See all results for RTX 3000 Ada Laptop 8GBSee all hardware for Ministral 8B
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