Can MPT-30B-Instruct run on Gaudi 3 128GB?

YES — Runs Great

A72Great

Estimated from fit model

MPT-30B-Instruct needs ~59.0 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q5_K_M quantization, expect ~122 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

Q5_K_M (High quality) — 59.0 GB, 122.3 tok/s, Runs well

59.0 GB required128.0 GB available

46% VRAM used

Fit status

Runs well

Decode

122.3 tok/s

TTFT

1583 ms

Safe context

Memory

59.0 GB / 128.0 GB

Memory breakdown

Weights21.6 GB

KV Cache23.4 GB

Runtime1.2 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsMPT-30B-Instruct on Gaudi 3 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 122.3 tok/s decode · 1.6s TTFT (warm) · 306 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	122.3 tok/s	863 ms	8K
Coding	A	Runs well	122.3 tok/s	1583 ms	8K
Agentic Coding	A	Runs well	122.3 tok/s	2302 ms	8K
Reasoning	A	Runs well	122.3 tok/s	1871 ms	8K
RAG	A	Runs well	122.3 tok/s	2878 ms	8K

Quantization options

How MPT-30B-Instruct (30B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	B59
Q3_K_S	3	14.7 GB	Low	B59
NVFP4	4	16.8 GB	Medium	B59
Q4_K_M	4	18.3 GB	Medium	B60
Q5_K_M	5	21.6 GB	High	B60
Q6_K	6	24.6 GB	High	B60
Q8_0	8	32.1 GB	Very High	B61
F16Best for your GPU	16	61.5 GB	Maximum	B66

Get started

Copy-paste commands to run MPT-30B-Instruct on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mosaicml/mpt-30b-instruct" \
  --hf-file "mpt-30b-instruct-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your Gaudi 3 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	37.5 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	391.6 tok/s
Qwen 3.5 122B A10B	122B	S	104.1 tok/s
Qwen 3.6 35B A3B	35B	S	329.1 tok/s
Qwen 3.5 35B A3B	35B	S	357.9 tok/s

Frequently asked questions

Can Gaudi 3 128GB run MPT-30B-Instruct?

Yes, Gaudi 3 128GB can run MPT-30B-Instruct with a A grade (Runs well). Expected decode speed: 122.3 tok/s.

How much VRAM does MPT-30B-Instruct need?

MPT-30B-Instruct (30B parameters) requires approximately 59.0 GB of memory with Q5_K_M quantization.

What is the best quantization for MPT-30B-Instruct?

The recommended quantization for MPT-30B-Instruct is Q5_K_M, which balances quality and memory efficiency.

What speed will MPT-30B-Instruct run at on Gaudi 3 128GB?

On Gaudi 3 128GB, MPT-30B-Instruct achieves approximately 122.3 tokens per second decode speed with a time-to-first-token of 1583ms using Q5_K_M quantization.

Can Gaudi 3 128GB run MPT-30B-Instruct for coding?

For coding workloads, MPT-30B-Instruct on Gaudi 3 128GB receives a A grade with 122.3 tok/s and 8K context.

What context window can MPT-30B-Instruct use on Gaudi 3 128GB?

On Gaudi 3 128GB, MPT-30B-Instruct can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if MPT-30B-Instruct 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 MPT-30B-Instruct?

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 128GB See all hardware for MPT-30B-Instruct

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