Can I Run Google Gemma 2 9B 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 9B Instruct (9.2B) 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 gemma2:9bLocal 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 9B 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 | 18.4 GB | 5.64 GB | 25.43 GB (32%) | ~124 t/s | FITS |
| Q8_08.5 bpw | 9.75 GB | 5.64 GB | 16.48 GB (21%) | ~224 t/s | FITS |
| Q6_K6.56 bpw | 7.54 GB | 5.64 GB | 14.19 GB (18%) | ~282 t/s | FITS |
| Q5_K_M5.68 bpw | 6.53 GB | 5.64 GB | 13.15 GB (16%) | ~320 t/s | FITS |
| Q4_K_M4.8 bpw | 5.52 GB | 5.64 GB | 12.1 GB (15%) | ~370 t/s | FITS |
| FP44.25 bpw | 4.88 GB | 5.64 GB | 11.44 GB (14%) | ~410 t/s | FITS |
| IQ3_XS3.3 bpw | 3.77 GB | 5.64 GB | 10.29 GB (13%) | ~506 t/s | FITS |
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Frequently Asked Questions: Google Gemma 2 9B Instruct on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)
How much VRAM does Google Gemma 2 9B Instruct need on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Google Gemma 2 9B Instruct requires 12.1 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Google Gemma 2 9B 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 370 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.