Can I Run Google Gemma 2 27B Instruct on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Google Gemma 2 27B Instruct (27.2B) on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (80 GB).
✓ Fits Comfortably in VRAM
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run gemma2:27bLocal Hardware Configurations & Pricing
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
NVIDIA H100 SXM (80GB HBM3 Cloud GPU)
Production vLLM high-concurrency serving & 70B FP8 inference
Cloud Instance Options for Google Gemma 2 27B Instruct
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 54.4 GB | 6.17 GB | 63.22 GB (79%) | ~44 t/s | FITS |
| Q8_08.5 bpw | 28.83 GB | 6.17 GB | 36.76 GB (46%) | ~81 t/s | FITS |
| Q6_K6.56 bpw | 22.3 GB | 6.17 GB | 30 GB (38%) | ~104 t/s | FITS |
| Q5_K_M5.68 bpw | 19.31 GB | 6.17 GB | 26.91 GB (34%) | ~119 t/s | FITS |
| Q4_K_M4.8 bpw | 16.32 GB | 6.17 GB | 23.81 GB (30%) | ~139 t/s | FITS |
| FP44.25 bpw | 14.42 GB | 6.17 GB | 21.84 GB (27%) | ~156 t/s | FITS |
| IQ3_XS3.3 bpw | 11.15 GB | 6.17 GB | 18.46 GB (23%) | ~199 t/s | FITS |
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Frequently Asked Questions: Google Gemma 2 27B Instruct on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)
How much VRAM does Google Gemma 2 27B Instruct need on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Google Gemma 2 27B Instruct requires 23.81 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Google Gemma 2 27B Instruct achieve on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?
Based on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)'s 3350 GB/s memory bandwidth, expected decode speed is approximately 139 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.