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URL: https://willitrunai.com/can-run/hf-unsloth--gemma-3-27b-it-gguf-on-gaudi-3-128gb

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Can gemma 3 27b it run on Gaudi 3 128GB?

YES — Runs Great

C48Usable
○Estimated from fit model

gemma 3 27b it needs ~33.3 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~157 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Share:

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) — 33.3 GB, 157.3 tok/s, Runs well
33.3 GB required128.0 GB available
26% VRAM used

Fit status

Runs well

Decode

157.3 tok/s

TTFT

1231 ms

Safe context

495K

Memory

33.3 GB / 128.0 GB

Memory breakdown

Weights16.5 GB
KV Cache3.2 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsgemma 3 27b it on Gaudi 3 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 157.3 tok/s decode · 1.2s TTFT (warm) · 393 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well157.3 tok/s672 ms495K
CodingCRuns well157.3 tok/s1231 ms495K
Agentic CodingCRuns well157.3 tok/s1791 ms495K
ReasoningCRuns well157.3 tok/s1455 ms495K
RAGCRuns well157.3 tok/s2238 ms495K

Quantization options

How gemma 3 27b it (27B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
10.5 GB
LowD39
Q3_K_S
3
13.2 GB
LowD39
NVFP4
4

Get started

Copy-paste commands to run gemma 3 27b it on your machine.

Run

lms load hf-unsloth--gemma-3-27b-it-gguf && lms server start

Frequently asked questions

See all results for Gaudi 3 128GBSee all hardware for gemma 3 27b it
15.1 GB
Medium
D39
Q4_K_M
4
16.5 GB
MediumD39
Q5_K_M
5
19.4 GB
HighD39
Q6_K
6
22.1 GB
HighD40
Q8_0
8
28.9 GB
Very HighC40
F16Best for your GPU
16
55.4 GB
MaximumC45

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.