Would CUDA be a better path than Gaudi 3 128GB for Llama 3.3 70B?

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.

Can Llama 3.3 70B run on Gaudi 3 128GB?

YES — Runs Great

S85Excellent

Estimated from fit model

Llama 3.3 70B needs ~61.3 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~61 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) — 61.3 GB, 66.0 tok/s, Runs well

61.3 GB required128.0 GB available

48% VRAM used

Fit status

Runs well

Decode

66.0 tok/s

TTFT

2935 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 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: 66.0 tok/s decode · 2.9s TTFT (warm) · 165 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	66.0 tok/s	1601 ms	128K
Coding	S	Runs well	60.7 tok/s	3192 ms	128K
Agentic Coding	S	Runs well	66.0 tok/s	4269 ms	128K
Reasoning	S	Runs well	66.0 tok/s	3469 ms	128K
RAG	S	Runs well	66.0 tok/s	5336 ms	128K

Quantization options

How Llama 3.3 70B (70B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A75
Q3_K_S	3	34.3 GB	Low	A76
NVFP4	4

Get started

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

Run

ollama run llama3.3

Your hardware

More models your Gaudi 3 128GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	37.5 tok/s
Qwen 3.5 122B A10B	122B	S

Frequently asked questions

Can Gaudi 3 128GB run Llama 3.3 70B?

Yes, Gaudi 3 128GB can run Llama 3.3 70B with a S grade (Runs well). Expected decode speed: 60.7 tok/s.

How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 61.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B?

The recommended quantization for Llama 3.3 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.3 70B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Llama 3.3 70B achieves approximately 60.7 tokens per second decode speed with a time-to-first-token of 3192ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Llama 3.3 70B for coding?

For coding workloads, Llama 3.3 70B on Gaudi 3 128GB receives a S grade with 60.7 tok/s and 128K context.

What context window can Llama 3.3 70B use on Gaudi 3 128GB?

On Gaudi 3 128GB, Llama 3.3 70B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.3 70B 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.

See all results for Gaudi 3 128GB See all hardware for Llama 3.3 70B

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