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URL: https://willitrunai.com/can-run/ministral-3-14b-on-rtx-pro-6000-blackwell-96gb

⇱ Ministral 3 14B on RTX PRO 6000 Blackwell Workstation Editi…


Can Ministral 3 14B run on RTX PRO 6000 Blackwell Workstation Edition 96GB?

YES — Runs Great

A82Great
Estimated from fit model

Ministral 3 14B needs ~23.0 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~152 tok/s.

Runtime: vLLMCapacity: RoomyBandwidth: HighStack: OptimizedBottleneck: Balanced
Share:

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) — 23.0 GB, 151.6 tok/s, Runs well
23.0 GB required96.0 GB available
24% VRAM used

Fit status

Runs well

Decode

151.6 tok/s

TTFT

1277 ms

Safe context

262K

Memory

23.0 GB / 96.0 GB

Memory breakdown

Weights8.5 GB
KV Cache2.4 GB
Runtime2.4 GB
Headroom9.6 GB

See how fast it feels

See how fast it feelsMinistral 3 14B on RTX PRO 6000 Blackwell Workstation Edition 96GB
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: 151.6 tok/s decode · 1.3s TTFT (warm) · 379 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
ChatARuns well151.6 tok/s697 ms262K
CodingARuns well151.6 tok/s1277 ms262K
Agentic CodingARuns well151.6 tok/s1858 ms262K
ReasoningARuns well151.6 tok/s1509 ms262K
RAGARuns well151.6 tok/s2322 ms262K

Quantization options

How Ministral 3 14B (14B params) fits at each quantization level on RTX PRO 6000 Blackwell Workstation Edition 96GB (96.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowA74
Q3_K_S
3
6.9 GB
LowA74
NVFP4
4
7.8 GB
MediumA74
Q4_K_M
4
8.5 GB
MediumA74
Q5_K_M
5
10.1 GB
HighA74
Q6_K
6
11.5 GB
HighA75
Q8_0
8
15.0 GB
Very HighA75
F16Best for your GPU
16
28.7 GB
MaximumA77

Get started

Copy-paste commands to run Ministral 3 14B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "mistralai/Ministral-3-14B-Instruct-2512" \ --hf-file "Ministral-3-14B-Instruct-2512-Q4_K_M.gguf" \ -c 4096 -ngl 99

Your hardware

More models your RTX PRO 6000 Blackwell Workstation Edition 96GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen3-Coder 30B A3B Instruct
30.5BS173 tok/s
👁 Alibaba
Qwen 3.5 27B
27BS79 tok/s
👁 Alibaba
Qwen 3.6 27B
27BS79.2 tok/s
👁 Alibaba
Qwen 3.5 122B A10B
122BS46 tok/s
👁 Alibaba
Qwen 3.6 35B A3B
35BS145.4 tok/s

Frequently asked questions

See all results for RTX PRO 6000 Blackwell Workstation Edition 96GBSee all hardware for Ministral 3 14B