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URL: https://willitrunai.com/can-run/hf-maziyarpanahi--gemma-3-27b-it-gguf-on-h200-pcie-141gb

⇱ gemma 3 27b it on NVIDIA H200 PCIe 141GB? YES

Can gemma 3 27b it run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

C47Usable
○Estimated from fit model

gemma 3 27b it needs ~34.9 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~245 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 34.9 GB, 244.8 tok/s, Runs well
34.9 GB required141.0 GB available
25% VRAM used

Fit status

Runs well

Decode

244.8 tok/s

TTFT

791 ms

Safe context

552K

Memory

34.9 GB / 141.0 GB

Memory breakdown

Weights16.5 GB
KV Cache3.2 GB
Runtime1.2 GB
Headroom14.1 GB

See how fast it feels

See how fast it feelsgemma 3 27b it on NVIDIA H200 PCIe 141GB
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: 244.8 tok/s decode · 791ms TTFT (warm) · 612 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well244.8 tok/s431 ms552K
CodingCRuns well244.8 tok/s791 ms552K
Agentic CodingCRuns well244.8 tok/s1150 ms552K
ReasoningCRuns well244.8 tok/s935 ms552K
RAGCRuns well244.8 tok/s1438 ms552K

Quantization options

How gemma 3 27b it (27B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
10.5 GB
LowD38
Q3_K_S
3
13.2 GB
LowD38
NVFP4
4
15.1 GB
MediumD38
Q4_K_M
4
16.5 GB
MediumD38
Q5_K_M
5
19.4 GB
HighD39
Q6_K
6
22.1 GB
HighD39
Q8_0
8
28.9 GB
Very HighD40
F16Best for your GPU
16
55.4 GB
MaximumC44

Get started

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

Run

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

Frequently asked questions

See all results for NVIDIA H200 PCIe 141GBSee all hardware for gemma 3 27b it