Can Mixtral 8x22B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

B68Good

○Estimated from fit model

Mixtral 8x22B needs ~103.1 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~49 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) — 103.1 GB, 48.7 tok/s, Runs well

103.1 GB required128.0 GB available

81% VRAM used

Fit status

Runs well

Decode

48.7 tok/s

TTFT

3974 ms

Safe context

66K

Memory

103.1 GB / 128.0 GB

Memory breakdown

Weights86.0 GB

KV Cache3.4 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsMixtral 8x22B on Intel Data Center GPU Max 1550 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: 48.7 tok/s decode · 4.0s TTFT (warm) · 122 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	48.7 tok/s	2168 ms	66K
Coding	B	Runs well	48.7 tok/s	3974 ms	66K
Agentic Coding	B	Tight fit	48.7 tok/s	5781 ms	66K
Reasoning	B	Runs well	48.7 tok/s	4697 ms	66K
RAG	B	Tight fit	48.7 tok/s	7226 ms	66K

Quantization options

How Mixtral 8x22B (141B 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	55.0 GB	Low	B59
Q3_K_S	3	69.1 GB	Low	B61
NVFP4	4

Get started

Copy-paste commands to run Mixtral 8x22B on your machine.

Run

ollama run mixtral:8x22b

Upgrade options

Hardware that runs Mixtral 8x22B well

👁 NVIDIA
NVIDIA H200 141GBBudget pick

141 GB VRAM (+13)4800 GB/s (+1600)

Raises estimated decode speed by about 100%.97.4 tok/s decode

Raises estimated decode speed by about 100%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$30,000 MSRP

👁 NVIDIA
NVIDIA H200 PCIe 141GBBest value