Can Ministral 3 14B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A81Great

Estimated from fit model

Ministral 3 14B needs ~25.6 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~196 tok/s.

Runtime: TransformersCapacity: RoomyBandwidth: HighStack: 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) — 25.6 GB, 196.0 tok/s, Runs well

25.6 GB required128.0 GB available

20% VRAM used

Fit status

Runs well

Decode

196.0 tok/s

TTFT

988 ms

Safe context

262K

Memory

25.6 GB / 128.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.4 GB

Runtime1.8 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsMinistral 3 14B on Intel Data Center GPU Max 1550 128GB
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: 196.0 tok/s decode · 988ms TTFT (warm) · 490 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	196.0 tok/s	539 ms	262K
Coding	A	Runs well	196.0 tok/s	988 ms	262K
Agentic Coding	A	Runs well	196.0 tok/s	1437 ms	262K
Reasoning	A	Runs well	196.0 tok/s	1167 ms	262K
RAG	A	Runs well	196.0 tok/s	1796 ms	262K

Inference speed

Ministral 3 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Ministral 3 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~121 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	120.9	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	87.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	77.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.1	Fits
RTX 3090 24GB	24 GB	Q4_K_M	66.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	61.5	Tight
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	58.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	55.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.1	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.1	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	30.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	27.7	Fits
RTX 4070 12GB	12 GB	Q4_K_M	25.9	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.3	Fits
RTX 3060 12GB	12 GB	Q4_K_M	16.3	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	4.5	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 14B (14B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A73
Q3_K_S	3	6.9 GB	Low	A73
NVFP4	4	7.8 GB	Medium	A73
Q4_K_M	4	8.5 GB	Medium	A73
Q5_K_M	5	10.1 GB	High	A73
Q6_K	6	11.5 GB	High	A73
Q8_0	8	15.0 GB	Very High	A74
F16Best for your GPU	16	28.7 GB	Maximum	A75

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 Intel Data Center GPU Max 1550 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	29.2 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	304.8 tok/s
Qwen 3.5 27B	27B	S	132.2 tok/s
Qwen 3.6 27B	27B	S	132.6 tok/s
Qwen 3.5 122B A10B	122B	S	81 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Ministral 3 14B?

Yes, Intel Data Center GPU Max 1550 128GB can run Ministral 3 14B with a A grade (Runs well). Expected decode speed: 196.0 tok/s.

How much VRAM does Ministral 3 14B need?

Ministral 3 14B (14B parameters) requires approximately 25.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 3 14B?

The recommended quantization for Ministral 3 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 3 14B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Ministral 3 14B achieves approximately 196.0 tokens per second decode speed with a time-to-first-token of 988ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Ministral 3 14B for coding?

For coding workloads, Ministral 3 14B on Intel Data Center GPU Max 1550 128GB receives a A grade with 196.0 tok/s and 262K context.

What context window can Ministral 3 14B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Ministral 3 14B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Ministral 3 14B feels slow on Intel Data Center GPU Max 1550 128GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB for Ministral 3 14B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Data Center GPU Max 1550 128GB See all hardware for Ministral 3 14B

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