Can Ministral 8B run on NVIDIA L4 24GB?

YES — Runs Great

B58Good

Estimated from fit model

Ministral 8B needs ~10.7 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 10.7 GB, 43.0 tok/s, Runs well

10.7 GB required24.0 GB available

45% VRAM used

Fit status

Runs well

Decode

43.0 tok/s

TTFT

4507 ms

Safe context

113K

Memory

10.7 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsMinistral 8B on NVIDIA L4 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: 43.0 tok/s decode · 4.5s TTFT (warm) · 107 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	B	Runs well	43.0 tok/s	2458 ms	113K
Coding	B	Runs well	43.0 tok/s	4507 ms	113K
Agentic Coding	B	Runs well	43.0 tok/s	6556 ms	113K
Reasoning	B	Runs well	43.0 tok/s	5326 ms	113K
RAG	B	Runs well	43.0 tok/s	8194 ms	113K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	C54
NVFP4	4	4.5 GB	Medium	C55
Q4_K_M	4	4.9 GB	Medium	C55
Q5_K_M	5	5.8 GB	High	B55
Q6_K	6	6.6 GB	High	B56
Q8_0	8	8.6 GB	Very High	B57
F16Best for your GPU	16	16.4 GB	Maximum	B59

Get started

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

Run

ollama run ministral

Upgrade options

Hardware that runs Ministral 8B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1492)

Raises estimated decode speed by about 160%.112 tok/s decode

Raises estimated decode speed by about 160%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 160%.112 tok/s decode

Raises estimated decode speed by about 160%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)576 GB/s (+276)

Raises estimated decode speed by about 136%.101.5 tok/s decode

Raises estimated decode speed by about 136%.

Adds memory headroom for longer context windows and future model growth.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Ministral 8B?

Yes, NVIDIA L4 24GB can run Ministral 8B with a B grade (Runs well). Expected decode speed: 43.0 tok/s.

How much VRAM does Ministral 8B need?

Ministral 8B (8B parameters) requires approximately 10.7 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Ministral 8B achieves approximately 43.0 tokens per second decode speed with a time-to-first-token of 4507ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on NVIDIA L4 24GB receives a B grade with 43.0 tok/s and 113K context.

What context window can Ministral 8B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Ministral 8B can safely use up to 113K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for Ministral 8B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

43.0 tok/s

2458 ms

113K

Coding

Runs well

43.0 tok/s

4507 ms

113K

Agentic Coding

Runs well

43.0 tok/s

6556 ms

113K

Reasoning

Runs well

43.0 tok/s

5326 ms

113K

RAG

Runs well

43.0 tok/s

8194 ms

113K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.1 GB

Low

C54

Q3_K_S

3.9 GB

Low

C54

NVFP4

4.5 GB

Medium

C55

Q4_K_M

4.9 GB

Medium

C55

Q5_K_M

5.8 GB

High

B55

Q6_K

6.6 GB

High

B56

Q8_0

8.6 GB

Very High

B57

F16Best for your GPU

16.4 GB

Maximum

B59

Frequently asked questions

Can NVIDIA L4 24GB run Ministral 8B?

Yes, NVIDIA L4 24GB can run Ministral 8B with a B grade (Runs well). Expected decode speed: 43.0 tok/s.

How much VRAM does Ministral 8B need?

Ministral 8B (8B parameters) requires approximately 10.7 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Ministral 8B achieves approximately 43.0 tokens per second decode speed with a time-to-first-token of 4507ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on NVIDIA L4 24GB receives a B grade with 43.0 tok/s and 113K context.

What context window can Ministral 8B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Ministral 8B can safely use up to 113K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.