Can DeepSeek Coder V2 16B run on Gaudi 3 128GB?

YES — Runs Great

A75Great

Estimated from fit model

DeepSeek Coder V2 16B needs ~26.8 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~632 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) — 26.8 GB, 631.8 tok/s, Runs well

26.8 GB required128.0 GB available

21% VRAM used

Fit status

Runs well

Decode

631.8 tok/s

TTFT

350 ms

Safe context

131K

Memory

26.8 GB / 128.0 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B 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: 631.8 tok/s decode · 350ms TTFT (warm) · 1580 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	631.8 tok/s	350 ms	131K
Coding	A	Runs well	631.8 tok/s	350 ms	131K
Agentic Coding	A	Runs well	631.8 tok/s	446 ms	131K
Reasoning	A	Runs well	631.8 tok/s	362 ms	131K
RAG	A	Runs well	631.8 tok/s	557 ms	131K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	B67
Q3_K_S	3	7.8 GB	Low	B67
NVFP4	4	9.0 GB	Medium	B67
Q4_K_M	4	9.8 GB	Medium	B67
Q5_K_M	5	11.5 GB	High	B67
Q6_K	6	13.1 GB	High	B67
Q8_0	8	17.1 GB	Very High	B67
F16Best for your GPU	16	32.8 GB	Maximum	B69

Get started

Copy-paste commands to run DeepSeek Coder V2 16B on your machine.

Run

lms load DeepSeek-Coder-V2-Lite-Instruct && lms server start

Your hardware

More models your Gaudi 3 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	37.5 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	391.6 tok/s
Qwen 3.5 27B	27B	S	169.8 tok/s
Qwen 3.6 27B	27B	S	105.9 tok/s
Qwen 3.5 122B A10B	122B	S	104.1 tok/s

Frequently asked questions

Can Gaudi 3 128GB run DeepSeek Coder V2 16B?

Yes, Gaudi 3 128GB can run DeepSeek Coder V2 16B with a A grade (Runs well). Expected decode speed: 631.8 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 26.8 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek Coder V2 16B?

The recommended quantization for DeepSeek Coder V2 16B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek Coder V2 16B run at on Gaudi 3 128GB?

On Gaudi 3 128GB, DeepSeek Coder V2 16B achieves approximately 631.8 tokens per second decode speed with a time-to-first-token of 350ms using Q4_K_M quantization.

Can Gaudi 3 128GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on Gaudi 3 128GB receives a A grade with 631.8 tok/s and 131K context.

What context window can DeepSeek Coder V2 16B use on Gaudi 3 128GB?

On Gaudi 3 128GB, DeepSeek Coder V2 16B 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 Coder V2 16B 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 DeepSeek Coder V2 16B?

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 DeepSeek Coder V2 16B

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