MODEL ARCHITECTURE SPEC • GOOGLE9.2B Dense
Google Gemma 2 9B Instruct VRAM & inference speed
9.2B Total ParamsDense Architecture42 Layers • 8 KV Heads8K Default Ctx (Max 8K)Ollama: gemma2:9b
Accurate memory requirements, KV cache footprint, and tokens/sec generation benchmarks for running Google Gemma 2 9B Instruct across local GPUs, Apple Silicon Macs, and cloud instances.
⚡ PARTIAL OFFLOAD12.1 GB (101%)~8 t/s
Google Gemma 2 9B Instruct on NVIDIA GeForce RTX 3060 (12GB)
⚠️ Partial Offload — Slow CPU/RAM Spillover
⚡ PARTIAL OFFLOAD
Required VRAM
12.1 GB
101% of hardware capacity (12 GB)Est. Decode Speed
~8 tok/s
Dense model @ 360 GB/sAvailable Headroom
-0.1 GB
Needs +0.1 GB more VRAMMemory Allocation & Headroom
12.1 GB allocated of 12 GB capacity
101% utilized
Weights: 5.52 GB
KV: (32K): 5.64 GB
Overhead: 0.94 GB
Deficit: -0.1 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~8 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 3060 (12GB) (360 GB/s). Prompt prefill / TTFT (~116 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run gemma2:9b⚠️ Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Google Gemma 2 9B Instruct @ 12.1 GB VRAM)
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) – $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) – $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)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 (212%) | 0 t/s | OOM |
| Q8_08.5 bpw | 9.75 GB | 5.64 GB | 16.48 GB (137%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 7.54 GB | 5.64 GB | 14.19 GB (118%) | ~6 t/s | PARTIAL |
| Q5_K_M5.68 bpw | 6.53 GB | 5.64 GB | 13.15 GB (110%) | ~7 t/s | PARTIAL |
| Q4_K_M4.8 bpw | 5.52 GB | 5.64 GB | 12.1 GB (101%) | ~8 t/s | PARTIAL |
| FP44.25 bpw | 4.88 GB | 5.64 GB | 11.44 GB (95%) | ~40 t/s | FITS |
| IQ3_XS3.3 bpw | 3.77 GB | 5.64 GB | 10.29 GB (86%) | ~49 t/s | FITS |
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Hardware Compatibility Matrix for Google Gemma 2 9B Instruct
Standard Q4_K_M precision with 32K context window and Q8_0 KV cache
| Hardware System | VRAM Envelope | Required VRAM | Headroom | Est. Decode Speed | Compatibility | Test Fit |
|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 5090 (32GB GDDR7) Consumer GPU • 1792 GB/s | 32 GB | 12.1 GB | +19.9 GB | ~187 tok/s | ✓ FITS | Test Fit → |
| NVIDIA GeForce RTX 4090 (24GB GDDR6X) Consumer GPU • 1008 GB/s | 24 GB | 12.1 GB | +11.9 GB | ~105 tok/s | ✓ FITS | Test Fit → |
| 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) Multi-GPU Rig • 936 GB/s | 48 GB | 12.1 GB | +35.9 GB | ~89 tok/s | ✓ FITS | Test Fit → |
| NVIDIA GeForce RTX 5080 (16GB GDDR7) Consumer GPU • 960 GB/s | 16 GB | 12.1 GB | +3.9 GB | ~100 tok/s | ✓ FITS | Test Fit → |
| NVIDIA RTX 4070 Ti Super (16GB) Consumer GPU • 672 GB/s | 16 GB | 12.1 GB | +3.9 GB | ~69 tok/s | ✓ FITS | Test Fit → |
| NVIDIA GeForce RTX 3060 (12GB) Consumer GPU • 360 GB/s | 12 GB | 12.1 GB | -0.1 GB | ~8 tok/s | ⚡ PARTIAL | Test Fit → |
| Apple Mac Mini M4 Pro (64GB Unified Memory) Apple Silicon • 273 GB/s | 54 GB | 12.1 GB | +41.9 GB | ~26 tok/s | ✓ FITS | Test Fit → |
| Apple Mac Studio M4 Max (128GB Unified Memory) Apple Silicon • 546 GB/s | 112 GB | 12.1 GB | +99.9 GB | ~54 tok/s | ✓ FITS | Test Fit → |
| Apple Mac Studio M5 Ultra (256GB Unified Memory) Apple Silicon • 890 GB/s | 236 GB | 12.1 GB | +223.9 GB | ~87 tok/s | ✓ FITS | Test Fit → |
| AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB) Unified APU / Mini-PC • 256 GB/s | 110 GB | 12.1 GB | +97.9 GB | ~22 tok/s | ✓ FITS | Test Fit → |
| NVIDIA DGX Spark (128GB Grace-Blackwell Unified) AI Appliance • 273 GB/s | 118 GB | 12.1 GB | +105.9 GB | ~28 tok/s | ✓ FITS | Test Fit → |
| NVIDIA H100 SXM (80GB HBM3 Cloud GPU) Datacenter Cloud GPU • 3350 GB/s | 80 GB | 12.1 GB | +67.9 GB | ~370 tok/s | ✓ FITS | Test Fit → |
| NVIDIA GeForce RTX 5070 Ti (16GB GDDR7) Consumer GPU • 896 GB/s | 16 GB | 12.1 GB | +3.9 GB | ~93 tok/s | ✓ FITS | Test Fit → |
| NVIDIA GeForce RTX 5070 (12GB GDDR7) Consumer GPU • 672 GB/s | 12 GB | 12.1 GB | -0.1 GB | ~15 tok/s | ⚡ PARTIAL | Test Fit → |
| NVIDIA GeForce RTX 4080 Super (16GB GDDR6X) Consumer GPU • 736 GB/s | 16 GB | 12.1 GB | +3.9 GB | ~77 tok/s | ✓ FITS | Test Fit → |
| AMD Radeon RX 7900 XTX (24GB GDDR6) Consumer GPU • 960 GB/s | 24 GB | 12.1 GB | +11.9 GB | ~85 tok/s | ✓ FITS | Test Fit → |
| AMD Radeon RX 7900 XT (20GB GDDR6) Consumer GPU • 800 GB/s | 20 GB | 12.1 GB | +7.9 GB | ~71 tok/s | ✓ FITS | Test Fit → |
| Apple Mac Studio M2 Ultra (192GB Unified Memory) Apple Silicon Workstation • 800 GB/s | 144 GB | 12.1 GB | +131.9 GB | ~80 tok/s | ✓ FITS | Test Fit → |