Can Kimi Linear 48B A3B run on Gaudi 3 128GB?

YES — Runs Great

A82Great

Estimated from fit model

Kimi Linear 48B A3B needs ~44.8 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~89 tok/s.

Runtime: TransformersCapacity: 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) — 44.8 GB, 88.5 tok/s, Runs well

44.8 GB required128.0 GB available

35% VRAM used

Fit status

Runs well

Decode

88.5 tok/s

TTFT

2189 ms

Safe context

1.0M

Memory

44.8 GB / 128.0 GB

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime1.8 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B 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: 88.5 tok/s decode · 2.2s TTFT (warm) · 221 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	88.5 tok/s	1194 ms	1.0M
Coding	A	Runs well	88.5 tok/s	2189 ms	1.0M
Agentic Coding	A	Runs well	88.5 tok/s	3183 ms	1.0M
Reasoning	A	Runs well	88.5 tok/s	2587 ms	1.0M
RAG	A	Runs well	88.5 tok/s	3979 ms	1.0M

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A71
Q3_K_S	3	23.5 GB	Low	A72
NVFP4	4	26.9 GB	Medium	A72
Q4_K_M	4	29.3 GB	Medium	A73
Q5_K_M	5	34.6 GB	High	A74
Q6_K	6	39.4 GB	High	A75
Q8_0	8	51.4 GB	Very High	A77
F16Best for your GPU	16	98.4 GB	Maximum	A80

Get started

Copy-paste commands to run Kimi Linear 48B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "moonshotai/Kimi-Linear-48B-A3B-Instruct" \
  --hf-file "Kimi-Linear-48B-A3B-Instruct-Q4_K_M.gguf" \
  -c 4096 -ngl 99

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
Command A 111B	111B	S	41.8 tok/s

Frequently asked questions

Can Gaudi 3 128GB run Kimi Linear 48B A3B?

Yes, Gaudi 3 128GB can run Kimi Linear 48B A3B with a A grade (Runs well). Expected decode speed: 88.5 tok/s.

How much VRAM does Kimi Linear 48B A3B need?

Kimi Linear 48B A3B (48B parameters) requires approximately 44.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Kimi Linear 48B A3B?

The recommended quantization for Kimi Linear 48B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Kimi Linear 48B A3B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, Kimi Linear 48B A3B achieves approximately 88.5 tokens per second decode speed with a time-to-first-token of 2189ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Kimi Linear 48B A3B for coding?

For coding workloads, Kimi Linear 48B A3B on Gaudi 3 128GB receives a A grade with 88.5 tok/s and 1.0M context.

What context window can Kimi Linear 48B A3B use on Gaudi 3 128GB?

On Gaudi 3 128GB, Kimi Linear 48B A3B can safely use up to 1.0M tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

What should I upgrade first if Kimi Linear 48B A3B 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 Kimi Linear 48B A3B?

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 Kimi Linear 48B A3B

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