Can Ministral 3 8B run on NVIDIA A16 64GB?

YES — Runs Great

A78Great

Estimated from fit model

Ministral 3 8B needs ~16.1 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~103 tok/s.

Runtime: SGLangCapacity: RoomyBandwidth: MediumStack: OptimizedBottleneck: 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) — 16.1 GB, 103.1 tok/s, Runs well

16.1 GB required64.0 GB available

25% VRAM used

Fit status

Runs well

Decode

103.1 tok/s

TTFT

1878 ms

Safe context

262K

Memory

16.1 GB / 64.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime2.6 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsMinistral 3 8B on NVIDIA A16 64GB
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: 103.1 tok/s decode · 1.9s TTFT (warm) · 258 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	A	Runs well	103.1 tok/s	1024 ms	262K
Coding	A	Runs well	103.1 tok/s	1878 ms	262K
Agentic Coding	A	Runs well	103.1 tok/s	2731 ms	262K
Reasoning	A	Runs well	103.1 tok/s	2219 ms	262K
RAG	A	Runs well	103.1 tok/s	3414 ms	262K

Quantization options

How Ministral 3 8B (8B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A71
Q3_K_S	3	3.9 GB	Low	A71
NVFP4	4	4.5 GB	Medium	A71
Q4_K_M	4	4.9 GB	Medium	A71
Q5_K_M	5	5.8 GB	High	A71
Q6_K	6	6.6 GB	High	A71
Q8_0	8	8.6 GB	Very High	A72
F16Best for your GPU	16	16.4 GB	Maximum	A73

Get started

Copy-paste commands to run Ministral 3 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Ministral-3-8B-Instruct-2512" \
  --hf-file "Ministral-3-8B-Instruct-2512-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your NVIDIA A16 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.8 tok/s
Qwen 3.5 27B	27B	S	30.7 tok/s
Qwen 3.6 27B	27B	S	30.8 tok/s
Qwen 3.6 35B A3B	35B	S	59.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	73.2 tok/s

Frequently asked questions

Can NVIDIA A16 64GB run Ministral 3 8B?

Yes, NVIDIA A16 64GB can run Ministral 3 8B with a A grade (Runs well). Expected decode speed: 103.1 tok/s.

How much VRAM does Ministral 3 8B need?

Ministral 3 8B (8B parameters) requires approximately 16.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 3 8B?

The recommended quantization for Ministral 3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 3 8B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Ministral 3 8B achieves approximately 103.1 tokens per second decode speed with a time-to-first-token of 1878ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Ministral 3 8B for coding?

For coding workloads, Ministral 3 8B on NVIDIA A16 64GB receives a A grade with 103.1 tok/s and 262K context.

What context window can Ministral 3 8B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Ministral 3 8B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for Ministral 3 8B

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