Can Ministral 8B run on NVIDIA H20 96GB?

YES — Runs Great

B55Good

Estimated from fit model

Ministral 8B needs ~17.9 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 17.9 GB, 112.0 tok/s, Runs well

17.9 GB required96.0 GB available

19% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

131K

Memory

17.9 GB / 96.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsMinistral 8B on NVIDIA H20 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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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	112.0 tok/s	943 ms	131K
Coding	B	Runs well	112.0 tok/s	1729 ms	131K
Agentic Coding	B	Runs well	112.0 tok/s	2514 ms	131K
Reasoning	B	Runs well	112.0 tok/s	2043 ms	131K
RAG	B	Runs well	112.0 tok/s	3143 ms	131K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C49
Q3_K_S	3	3.9 GB	Low	C49
NVFP4	4	4.5 GB	Medium	C49
Q4_K_M	4	4.9 GB	Medium	C49
Q5_K_M	5	5.8 GB	High	C49
Q6_K	6	6.6 GB	High	C49
Q8_0	8	8.6 GB	Very High	C49
F16Best for your GPU	16	16.4 GB	Maximum	C50

Get started

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

Run

ollama run ministral

Opciones de mejora

Hardware que ejecuta bien Ministral 8B

Mac Studio M3 Ultra 256GBOpción económica

256 GB Unified (+160)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.112 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$6,999 MSRP

Frequently asked questions

Can NVIDIA H20 96GB run Ministral 8B?

Yes, NVIDIA H20 96GB can run Ministral 8B with a B grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Ministral 8B need?

Ministral 8B (8B parameters) requires approximately 17.9 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 NVIDIA H20 96GB?

On NVIDIA H20 96GB, Ministral 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on NVIDIA H20 96GB receives a B grade with 112.0 tok/s and 131K context.

What context window can Ministral 8B use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Ministral 8B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA H20 96GB See all hardware for Ministral 8B

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