Can Ministral 3 14B run on RX 7800 XT 16GB?

YES — Tight Fit

S87Excellent

○Estimated from fit model

Ministral 3 14B needs ~14.4 GB VRAM. RX 7800 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: TransformersCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 14.4 GB, 48.7 tok/s, Tight fit

14.4 GB required16.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

48.7 tok/s

TTFT

3975 ms

Safe context

27K

Memory

14.4 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.4 GB

Runtime1.8 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsMinistral 3 14B on RX 7800 XT 16GB
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: 48.7 tok/s decode · 4.0s TTFT (warm) · 122 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	S	Tight fit	48.7 tok/s	2168 ms	27K
Coding	S	Tight fit	48.7 tok/s	3975 ms	27K
Agentic Coding	S	Runs with offload (needs ~0.4 GB host RAM)	32.9 tok/s	8568 ms	27K
Reasoning	S	Tight fit	48.7 tok/s	4698 ms	27K
RAG	S	Runs with offload (needs ~0.4 GB host RAM)	32.9 tok/s	10710 ms	27K

Quantization options

How Ministral 3 14B (14B params) fits at each quantization level on RX 7800 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A84
Q3_K_S	3	6.9 GB	Low	S86
NVFP4	4	7.8 GB	Medium	S87
Q4_K_M	4	8.5 GB	Medium	S86
Q5_K_M	5	10.1 GB	High	S86
Q6_KBest for your GPU	6	11.5 GB	High	S86
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

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 RX 7800 XT 16GB can run

Model	Params	Grade	Decode	Capabilities
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	46.4 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	40.3 tok/s

Frequently asked questions

See all results for RX 7800 XT 16GB See all hardware for Ministral 3 14B

URL: https://willitrunai.com/can-run/ministral-3-14b-on-rx-7800-xt-16gb

⇱ Ministral 3 14B on RX 7800 XT 16GB? TIGHT FIT