Can Qwen 3 235B A22B run on Intel Data Center GPU Max 1550 128GB?

YES — With NVFP4

A78Great

○Estimated from fit model

Qwen 3 235B A22B needs ~148.2 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With NVFP4 quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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.

Qwen 3 235B A22B at Q4_K_M needs 159.9 GB — too much for Intel Data Center GPU Max 1550 128GB (128.0 GB). Runs at NVFP4 (148.2 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 159.9 GB, exceeds 128.0 GB available

159.9 GB required128.0 GB available

125% VRAM needed

31.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

19.3 tok/s

TTFT

10052 ms

Safe context

Memory

159.9 GB / 128.0 GB

Offload

20%

Memory breakdown

Weights143.4 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3 235B A22B 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: 19.3 tok/s decode · 10.1s TTFT (warm) · 48 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	19.6 tok/s	5380 ms	4K
Coding	F	Too heavy	19.3 tok/s	10052 ms	4K
Agentic Coding	F	Too heavy	18.6 tok/s	15179 ms	4K
Reasoning	F	Too heavy	19.3 tok/s	11880 ms	4K
RAG	F	Too heavy	18.6 tok/s	18974 ms	4K

Quantization options

How Qwen 3 235B A22B (235B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	91.7 GB	Low	S86
Q3_K_S	3	115.2 GB	Low	F0

Get started

Copy-paste commands to run Qwen 3 235B A22B on your machine.

Run

lms load Qwen3-235B-A22B-Instruct-2507 && lms server start

Upgrade options

Hardware that runs Qwen 3 235B A22B well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)

Makes the model fit on the accelerator instead of staying completely out of reach.11.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$6,999 MSRP

AMD Instinct MI350X 288GBBest value

288 GB VRAM (+160)8000 GB/s (+4800)

Makes the model fit on the accelerator instead of staying completely out of reach.118.9 tok/s decode