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⇱ Llama 3.3 70B on Intel Data Center GPU Max 1550 128GB? YES

Can Llama 3.3 70B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A85Great
○Estimated from fit model

Llama 3.3 70B needs ~61.3 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Share:

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) — 61.3 GB, 51.3 tok/s, Runs well
61.3 GB required128.0 GB available
48% VRAM used

Fit status

Runs well

Decode

51.3 tok/s

TTFT

3771 ms

Safe context

128K

Memory

61.3 GB / 128.0 GB

Memory breakdown

Weights42.7 GB
KV Cache4.9 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B 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: 51.3 tok/s decode · 3.8s TTFT (warm) · 128 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
ChatARuns well51.3 tok/s2057 ms128K
CodingARuns well51.3 tok/s3771 ms128K
Agentic CodingSRuns well51.3 tok/s5485 ms128K
ReasoningARuns well51.3 tok/s4456 ms128K
RAGSRuns well51.3 tok/s6856 ms128K

Quantization options

How Llama 3.3 70B (70B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowA75
Q3_K_S
3
34.3 GB
LowA76
NVFP4
4
39.2 GB
MediumA77
Q4_K_M
4
42.7 GB
MediumA77
Q5_K_M
5
50.4 GB
HighA79
Q6_K
6
57.4 GB
HighA80
Q8_0Best for your GPU
8
74.9 GB
Very HighA82
F16
16
143.5 GB
MaximumF0

Get started

Copy-paste commands to run Llama 3.3 70B on your machine.

Run

ollama run llama3.3

Your hardware

More models your Intel Data Center GPU Max 1550 128GB can run

ModelParamsGradeDecodeCapabilities
👁 Mistral
Devstral 2 123B Instruct		123BS29.2 tok/s
👁 Alibaba
Qwen 3.5 122B A10B		122BS81 tok/s
👁 Mistral
Mistral Small 4 119B		119BS87.9 tok/s
👁 OpenAI
GPT-OSS 120B		117BS30.7 tok/s
👁 Cohere
Command A 111B		111BS32.5 tok/s

Frequently asked questions

See all results for Intel Data Center GPU Max 1550 128GBSee all hardware for Llama 3.3 70B