Can Ministral 8B run on RTX 5070 12GB?

YES — Runs Great

B66Good

Estimated from fit model

Ministral 8B needs ~9.5 GB VRAM. RTX 5070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~93 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 9.5 GB, 93.3 tok/s, Runs well

9.5 GB required12.0 GB available

79% VRAM used

Fit status

Runs well

Decode

93.3 tok/s

TTFT

2076 ms

Safe context

34K

Memory

9.5 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsMinistral 8B on RTX 5070 12GB
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: 93.3 tok/s decode · 2.1s TTFT (warm) · 233 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	93.3 tok/s	1132 ms	34K
Coding	B	Runs well	93.3 tok/s	2076 ms	34K
Agentic Coding	B	Runs with offload	93.3 tok/s	3019 ms	34K
Reasoning	B	Runs well	93.3 tok/s	2453 ms	34K
RAG	B	Runs with offload	93.3 tok/s	3774 ms	34K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on RTX 5070 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B59
Q3_K_S	3	3.9 GB	Low	B60
NVFP4	4	4.5 GB	Medium	B61
Q4_K_M	4	4.9 GB	Medium	B61
Q5_K_M	5	5.8 GB	High	B62
Q6_K	6	6.6 GB	High	B62
Q8_0Best for your GPU	8	8.6 GB	Very High	B61
F16	16	16.4 GB	Maximum	F0

Get started

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

Run

ollama run ministral

Frequently asked questions

Can RTX 5070 12GB run Ministral 8B?

Yes, RTX 5070 12GB can run Ministral 8B with a B grade (Runs well). Expected decode speed: 93.3 tok/s.

How much VRAM does Ministral 8B need?

Ministral 8B (8B parameters) requires approximately 9.5 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 5070 12GB?

On RTX 5070 12GB, Ministral 8B achieves approximately 93.3 tokens per second decode speed with a time-to-first-token of 2076ms using Q4_K_M quantization.

Can RTX 5070 12GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on RTX 5070 12GB receives a B grade with 93.3 tok/s and 34K context.

What context window can Ministral 8B use on RTX 5070 12GB?

On RTX 5070 12GB, Ministral 8B can safely use up to 34K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 5070 12GB See all hardware for Ministral 8B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/ministral-8b-on-rtx-5070-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

93.3 tok/s

1132 ms

34K

Coding

Runs well

93.3 tok/s

2076 ms

34K

Agentic Coding

Runs with offload

93.3 tok/s

3019 ms

34K

Reasoning

Runs well

93.3 tok/s

2453 ms

34K

RAG

Runs with offload

93.3 tok/s

3774 ms

34K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.1 GB

Low

B59

Q3_K_S

3.9 GB

Low

B60

NVFP4

4.5 GB

Medium

B61

Q4_K_M

4.9 GB

Medium

B61

Q5_K_M

5.8 GB

High

B62

Q6_K

6.6 GB

High

B62

Q8_0Best for your GPU

8.6 GB

Very High

B61

F16

16.4 GB

Maximum

Frequently asked questions

Can RTX 5070 12GB run Ministral 8B?

Yes, RTX 5070 12GB can run Ministral 8B with a B grade (Runs well). Expected decode speed: 93.3 tok/s.

How much VRAM does Ministral 8B need?

Ministral 8B (8B parameters) requires approximately 9.5 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 5070 12GB?

On RTX 5070 12GB, Ministral 8B achieves approximately 93.3 tokens per second decode speed with a time-to-first-token of 2076ms using Q4_K_M quantization.

Can RTX 5070 12GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on RTX 5070 12GB receives a B grade with 93.3 tok/s and 34K context.

What context window can Ministral 8B use on RTX 5070 12GB?

On RTX 5070 12GB, Ministral 8B can safely use up to 34K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.