Will It Run AI

Can Ministral 8B run on RX 6700 XT 12GB?

YES — Runs Great

B64Good
Estimated from fit model

Ministral 8B needs ~9.2 GB VRAM. RX 6700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~44 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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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) — 9.2 GB, 44.0 tok/s, Runs well
9.2 GB required12.0 GB available
77% VRAM used

Fit status

Runs well

Decode

44.0 tok/s

TTFT

4401 ms

Safe context

37K

Memory

9.2 GB / 12.0 GB

Memory breakdown

Weights4.9 GB
KV Cache2.2 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsMinistral 8B on RX 6700 XT 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: 44.0 tok/s decode · 4.4s TTFT (warm) · 110 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well44.0 tok/s2401 ms37K
CodingBRuns well44.0 tok/s4401 ms37K
Agentic CodingBTight fit44.0 tok/s6402 ms37K
ReasoningBRuns well44.0 tok/s5202 ms37K
RAGBTight fit44.0 tok/s8002 ms37K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on RX 6700 XT 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowB59
Q3_K_S
3
3.9 GB
LowB60
NVFP4
4
4.5 GB
MediumB61
Q4_K_M
4
4.9 GB
MediumB61
Q5_K_M
5
5.8 GB
HighB62
Q6_K
6
6.6 GB
HighB62
Q8_0Best for your GPU
8
8.6 GB
Very HighB61
F16
16
16.4 GB
MaximumF0

Get started

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

Run

ollama run ministral

Opções de upgrade

Hardware que roda bem Ministral 8B

RX 9070 16GBOpção econômica
16 GB VRAM (+4)640 GB/s (+256)
B
Raises estimated decode speed by about 99%.87.4 tok/s decodificação

Raises estimated decode speed by about 99%.

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

~$479 MSRP

RX 7800 XT 16GBMelhor custo-benefício
16 GB VRAM (+4)624 GB/s (+240)
B
Raises estimated decode speed by about 94%.85.2 tok/s decodificação

Raises estimated decode speed by about 94%.

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

~$499 MSRP

RX 9070 XT 16GBUpgrade AMD
16 GB VRAM (+4)640 GB/s (+256)
B
Raises estimated decode speed by about 105%.90.2 tok/s decodificação

Raises estimated decode speed by about 105%.

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

~$549 MSRP

Frequently asked questions

Can RX 6700 XT 12GB run Ministral 8B?

Yes, RX 6700 XT 12GB can run Ministral 8B with a B grade (Runs well). Expected decode speed: 44.0 tok/s.

How much VRAM does Ministral 8B need?

Ministral 8B (8B parameters) requires approximately 9.2 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 RX 6700 XT 12GB?

On RX 6700 XT 12GB, Ministral 8B achieves approximately 44.0 tokens per second decode speed with a time-to-first-token of 4401ms using Q4_K_M quantization.

Can RX 6700 XT 12GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on RX 6700 XT 12GB receives a B grade with 44.0 tok/s and 37K context.

What context window can Ministral 8B use on RX 6700 XT 12GB?

On RX 6700 XT 12GB, Ministral 8B can safely use up to 37K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RX 6700 XT 12GBSee all hardware for Ministral 8B
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