Can Qwen3-Coder-Next run on Gaudi 3 128GB?

YES — Runs Great

S92Excellent

○Estimated from fit model

Qwen3-Coder-Next needs ~64.0 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~175 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) — 64.0 GB, 174.9 tok/s, Runs well

64.0 GB required128.0 GB available

50% VRAM used

Fit status

Runs well

Decode

174.9 tok/s

TTFT

1107 ms

Safe context

256K

Memory

64.0 GB / 128.0 GB

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next 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: 174.9 tok/s decode · 1.1s TTFT (warm) · 437 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	160.8 tok/s	657 ms	256K
Coding	S	Runs well	174.9 tok/s	1107 ms	256K
Agentic Coding	S	Runs well	174.9 tok/s	1610 ms	256K
Reasoning	S	Runs well	174.9 tok/s	1308 ms	256K
RAG	S	Runs well	174.9 tok/s	2013 ms	256K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	A81
Q3_K_S	3	39.2 GB	Low	A82
NVFP4	4

Get started

Copy-paste commands to run Qwen3-Coder-Next on your machine.

Run

ollama run qwen3-coder-next

Your hardware

More models your Gaudi 3 128GB can run

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

Frequently asked questions

See all results for Gaudi 3 128GB See all hardware for Qwen3-Coder-Next

URL: https://willitrunai.com/can-run/qwen-3-coder-next-on-gaudi-3-128gb

⇱