Will It Run AI

Can Ministral 8B run on Gaudi 3 128GB?

YES — Runs Great

C55Usable
Estimated from fit model

Ministral 8B needs ~20.8 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 20.8 GB, 112.0 tok/s, Runs well
20.8 GB required128.0 GB available
16% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

131K

Memory

20.8 GB / 128.0 GB

Memory breakdown

Weights4.9 GB
KV Cache2.2 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsMinistral 8B on Gaudi 3 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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well112.0 tok/s943 ms131K
CodingCRuns well112.0 tok/s1729 ms131K
Agentic CodingBRuns well112.0 tok/s2514 ms131K
ReasoningCRuns well112.0 tok/s2043 ms131K
RAGBRuns well112.0 tok/s3143 ms131K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC48
Q3_K_S
3
3.9 GB
LowC48
NVFP4
4
4.5 GB
MediumC48
Q4_K_M
4
4.9 GB
MediumC48
Q5_K_M
5
5.8 GB
HighC48
Q6_K
6
6.6 GB
HighC48
Q8_0
8
8.6 GB
Very HighC48
F16Best for your GPU
16
16.4 GB
MaximumC48

Get started

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

Run

ollama run ministral

アップグレードオプション

Ministral 8Bを快適に動かすハードウェア

Mac Studio M3 Ultra 256GBコスパ重視
256 GB Unified (+128)
B
Adds memory headroom for longer context windows and future model growth.112 tok/sデコード

Adds memory headroom for longer context windows and future model growth.

〜$6,999 MSRP

Frequently asked questions

Can Gaudi 3 128GB run Ministral 8B?

Yes, Gaudi 3 128GB can run Ministral 8B with a C grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Ministral 8B need?

Ministral 8B (8B parameters) requires approximately 20.8 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 Gaudi 3 128GB?

On Gaudi 3 128GB, 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 Gaudi 3 128GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on Gaudi 3 128GB receives a C grade with 112.0 tok/s and 131K context.

What context window can Ministral 8B use on Gaudi 3 128GB?

On Gaudi 3 128GB, 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.

What should I upgrade first if Ministral 8B feels slow on Gaudi 3 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 Gaudi 3 128GB for Ministral 8B?

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 Gaudi 3 128GBSee all hardware for Ministral 8B
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