Would CUDA be a better path than Gaudi 3 128GB for DeepSeek R1 Distill 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 DeepSeek R1 Distill 70B run on Gaudi 3 128GB?

Q: What should I upgrade first if DeepSeek R1 Distill 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.

YES — Runs Great

A77Great

Estimated from fit model

DeepSeek R1 Distill 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

131K

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 feelsDeepSeek R1 Distill 70B on Gaudi 3 128GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
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	60.7 tok/s	1741 ms	131K
Coding	A	Runs well	60.7 tok/s	3192 ms	131K
Agentic Coding	A	Runs well	60.7 tok/s	4643 ms	131K
Reasoning	A	Runs well	60.7 tok/s	3772 ms	131K
RAG	A	Runs well	60.7 tok/s	5803 ms	131K

Quantization options

How DeepSeek R1 Distill 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	B67
Q3_K_S	3	34.3 GB	Low	B68
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 Distill 70B on your machine.

Run

ollama run deepseek-r1:70b

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 DeepSeek R1 Distill 70B?

Yes, Gaudi 3 128GB can run DeepSeek R1 Distill 70B with a A grade (Runs well). Expected decode speed: 60.7 tok/s.

How much VRAM does DeepSeek R1 Distill 70B need?

DeepSeek R1 Distill 70B (70B parameters) requires approximately 61.3 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 70B?

The recommended quantization for DeepSeek R1 Distill 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 70B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, DeepSeek R1 Distill 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 DeepSeek R1 Distill 70B for coding?

For coding workloads, DeepSeek R1 Distill 70B on Gaudi 3 128GB receives a A grade with 60.7 tok/s and 131K context.

What context window can DeepSeek R1 Distill 70B use on Gaudi 3 128GB?

On Gaudi 3 128GB, DeepSeek R1 Distill 70B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek R1 Distill 70B feels slow on Gaudi 3 128GB?

See all results for Gaudi 3 128GB See all hardware for DeepSeek R1 Distill 70B

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