Can Ministral 3 8B run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

A76Great

Estimated from fit model

Ministral 3 8B needs ~23.8 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: SGLangCapacity: RoomyBandwidth: HighStack: 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) — 23.8 GB, 112.0 tok/s, Runs well

23.8 GB required141.0 GB available

17% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

262K

Memory

23.8 GB / 141.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime2.6 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsMinistral 3 8B on NVIDIA H200 PCIe 141GB
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	A	Runs well	112.0 tok/s	943 ms	262K
Coding	A	Runs well	112.0 tok/s	1729 ms	262K
Agentic Coding	A	Runs well	112.0 tok/s	2514 ms	262K
Reasoning	A	Runs well	112.0 tok/s	2043 ms	262K
RAG	A	Runs well	112.0 tok/s	3143 ms	262K

Inference speed

Ministral 3 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Ministral 3 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	102.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	96.9	Fits
RTX 4070 12GB	12 GB	Q4_K_M	83.3	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	82.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	82.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	52.9	Fits
RTX 3060 12GB	12 GB	Q4_K_M	52.3	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	48.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	42.6	Fits
RTX 4060 8GB	8 GB	Q4_K_M	15.5	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Ministral 3 8B (8B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B68
Q3_K_S	3	3.9 GB	Low	B68
NVFP4	4	4.5 GB	Medium	B68
Q4_K_M	4	4.9 GB	Medium	B68
Q5_K_M	5	5.8 GB	High	B68
Q6_K	6	6.6 GB	High	B68
Q8_0	8	8.6 GB	Very High	B68
F16Best for your GPU	16	16.4 GB	Maximum	B69

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 H200 PCIe 141GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	58.4 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	609.7 tok/s
Qwen 3.5 27B	27B	S	264.4 tok/s
Qwen 3.6 27B	27B	S	265.2 tok/s
Qwen 3.5 122B A10B	122B	S	162.1 tok/s

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run Ministral 3 8B?

Yes, NVIDIA H200 PCIe 141GB can run Ministral 3 8B with a A grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Ministral 3 8B need?

Ministral 3 8B (8B parameters) requires approximately 23.8 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 H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Ministral 3 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 H200 PCIe 141GB run Ministral 3 8B for coding?

For coding workloads, Ministral 3 8B on NVIDIA H200 PCIe 141GB receives a A grade with 112.0 tok/s and 262K context.

What context window can Ministral 3 8B use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, 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 H200 PCIe 141GB See all hardware for Ministral 3 8B

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