Can Mixtral 8x7B run on Gaudi 3 128GB?

YES — Runs Great

B65Good

○Estimated from fit model

Mixtral 8x7B needs ~44.3 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~173 tok/s.

Runtime: llama.cppCapacity: 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) — 44.3 GB, 186.3 tok/s, Runs well

44.3 GB required128.0 GB available

35% VRAM used

Fit status

Runs well

Decode

186.3 tok/s

TTFT

1039 ms

Safe context

33K

Memory

44.3 GB / 128.0 GB

Memory breakdown

Weights28.7 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsMixtral 8x7B 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: 186.3 tok/s decode · 1.0s TTFT (warm) · 466 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	B	Runs well	173.3 tok/s	609 ms	33K
Coding	B	Runs well	173.3 tok/s	1117 ms	33K
Agentic Coding	B	Runs well	173.3 tok/s	1625 ms	33K
Reasoning	B	Runs well	173.3 tok/s	1320 ms	33K
RAG	B	Runs well	173.3 tok/s	2031 ms	33K

Quantization options

How Mixtral 8x7B (47B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.3 GB	Low	C54
Q3_K_S	3	23.0 GB	Low	C55
NVFP4	4

Get started

Copy-paste commands to run Mixtral 8x7B on your machine.

Run

ollama run mixtral

Frequently asked questions

See all results for Gaudi 3 128GB See all hardware for Mixtral 8x7B

URL: https://willitrunai.com/can-run/mixtral-8x7b-on-gaudi-3-128gb

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