Can Command A 111B run on Gaudi 3 128GB?

YES — Runs Great

S94Excellent

Estimated from fit model

Command A 111B needs ~85.3 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~42 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) — 85.3 GB, 41.8 tok/s, Runs well

85.3 GB required128.0 GB available

67% VRAM used

Fit status

Runs well

Decode

41.8 tok/s

TTFT

4636 ms

Safe context

191K

Memory

85.3 GB / 128.0 GB

Memory breakdown

Weights67.7 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsCommand A 111B 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: 41.8 tok/s decode · 4.6s TTFT (warm) · 104 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	S	Runs well	41.8 tok/s	2529 ms	191K
Coding	S	Runs well	41.8 tok/s	4636 ms	191K
Agentic Coding	S	Runs well	41.8 tok/s	6744 ms	191K
Reasoning	S	Runs well	41.8 tok/s	5479 ms	191K
RAG	S	Runs well	41.8 tok/s	8430 ms	191K

Quantization options

How Command A 111B (111B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	43.3 GB	Low	A84
Q3_K_S	3	54.4 GB	Low	S86
NVFP4	4	62.2 GB	Medium	S87
Q4_K_M	4	67.7 GB	Medium	S88
Q5_K_M	5	79.9 GB	High	S88
Q6_KBest for your GPU	6	91.0 GB	High	S88
Q8_0	8	118.8 GB	Very High	F0
F16	16	227.6 GB	Maximum	F0

Get started

Copy-paste commands to run Command A 111B on your machine.

Run

ollama run command-a

Your hardware

More models your Gaudi 3 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	37.5 tok/s
Qwen 3.5 122B A10B	122B	S	104.1 tok/s
Mistral Small 4 119B	119B	S	112.9 tok/s
GPT-OSS 120B	117B	S	39.5 tok/s

Frequently asked questions

Can Gaudi 3 128GB run Command A 111B?

Yes, Gaudi 3 128GB can run Command A 111B with a S grade (Runs well). Expected decode speed: 41.8 tok/s.

How much VRAM does Command A 111B need?

Command A 111B (111B parameters) requires approximately 85.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Command A 111B?

The recommended quantization for Command A 111B is Q4_K_M, which balances quality and memory efficiency.

What speed will Command A 111B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Command A 111B achieves approximately 41.8 tokens per second decode speed with a time-to-first-token of 4636ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Command A 111B for coding?

For coding workloads, Command A 111B on Gaudi 3 128GB receives a S grade with 41.8 tok/s and 191K context.

What context window can Command A 111B use on Gaudi 3 128GB?

On Gaudi 3 128GB, Command A 111B can safely use up to 191K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Command A 111B 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.

Would CUDA be a better path than Gaudi 3 128GB for Command A 111B?

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.

See all results for Gaudi 3 128GB See all hardware for Command A 111B

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