Google Gemmaby Google DeepMind
Gemma 2 27B VRAM & Cloud GPU Sizer
Google DeepMind's highly efficient architecture combining sliding-window and global attention. Use this interactive calculator to compute exact GPU memory requirements across FP16, FP8, INT4, QLoRA, and Full Fine-Tuning.
Inference VRAM (INT4 AWQ)
~18.61 GB
1x RTX 5090 (32GB) or 1x L40S (48GB)Native Precision (FP16 / BF16)
~55.34 GB
Fits on 1x 24GB GPUQLoRA 4-bit Training
~27.29 GB
1x A100 80GB (QLoRA)Interactive VRAM Sizer & Live Cloud Pricing Matrix Active
Popular Model Presets
Auto-ArchitectedB
0.5B (Edge)7B14B32B70B140B+
2 B/param
tokens
reqs
Minimum Required VRAMActive
55.34GB
Recommended Target:63.64 GB(+15% buffer)
Suggested Minimum GPU Tier:
1x A100 / H100 (80GB)
VRAM Memory Footprint Breakdown
Sum: 55.34 GBModel Weights
50.66 GB
KV-Cache Context
2.88 GB
Activations
0.3 GB
CUDA & Runtime
1.5 GB
Estimated Decoding Speed (Tokens/sec):
A100 (80GB): 26.1 t/sH100 (80GB): 42.9 t/s
Live Cloud GPU Cost & Pricing Engine
19 Available NodesReal-time verified pricing across RunPod, Lambda Labs, Vast.ai, AWS, GCP, and specialized clouds.
Lowest Cost Compatible OptionNVIDIA A100 SXM4 (80GB) on Lambda Labs (80GB VRAM)
Spot Rate$1.25 / 1h
Rent NodeBuy vs. Rent TCO LabHardware break-even simulator
Providers:
| GPU & Architecture | Provider | Total VRAM | Spot (1h) | On-Demand (1h) | Action |
|---|---|---|---|---|---|
NVIDIA A100 SXM4 (80GB)Popular Ampere•NVLink-3 (600 GB/s) | Lambda Labs | 80 GB | $1.25($1.25/hr) | $1.69($1.69/hr) | Rent |
2x NVIDIA RTX 5090 Blackwell•PCIe 5.0 | RunPod | 64 GB(2x 32GB) | $1.38($1.38/hr) | $1.98($1.98/hr) | Rent |
NVIDIA A100 SXM4 (80GB) Ampere•NVLink-3 (600 GB/s) | RunPod | 80 GB | $1.39($1.39/hr) | $1.89($1.89/hr) | Rent |
NVIDIA A100 SXM4 (80GB) Ampere•NVLink-3 (600 GB/s) | GCP (a2-ultragpu-1g) | 80 GB | $1.75($1.75/hr) | $3.67($3.67/hr) | Rent |
4x NVIDIA RTX 4090 Ada Lovelace•PCIe 4.0 | RunPod | 96 GB(4x 24GB) | $1.76($1.76/hr) | $2.76($2.76/hr) | Rent |
NVIDIA H100 SXM5 (80GB) Hopper•NVLink-4 (900 GB/s) + InfiniBand | Nebius AI Studio | 80 GB | $2.19($2.19/hr) | $2.85($2.85/hr) | Rent |
NVIDIA H100 SXM5 (80GB)Popular Hopper•NVLink-4 (900 GB/s) | Lambda Labs | 80 GB | $2.29($2.29/hr) | $2.99($2.99/hr) | Rent |
NVIDIA H100 SXM5 (80GB) Hopper•NVLink-4 (900 GB/s) | CoreWeave | 80 GB | $2.35($2.35/hr) | $3.15($3.15/hr) | Rent |
NVIDIA H100 SXM5 (80GB)Popular Hopper•NVLink-4 (900 GB/s) | RunPod | 80 GB | $2.49($2.49/hr) | $3.29($3.29/hr) | Rent |
4x NVIDIA RTX 5090 Blackwell•PCIe 5.0 | RunPod | 128 GB(4x 32GB) | $2.76($2.76/hr) | $3.96($3.96/hr) | Rent |
NVIDIA H200 SXM5 (141GB)Popular Hopper HBM3e•NVLink-4 (900 GB/s) | Lambda Labs | 141 GB | $3.49($3.49/hr) | $4.29($4.29/hr) | Rent |
8x NVIDIA A100 SXM4 (80GB) Ampere•NVLink-3 (600 GB/s) | Lambda Labs | 640 GB(8x 80GB) | $9.90($9.90/hr) | $13.52($13.52/hr) | Rent |
8x NVIDIA A100 SXM4 (80GB) Ampere•NVLink-3 (600 GB/s) | AWS (EC2 p4de.24xlarge) | 640 GB(8x 80GB) | $14.50($14.50/hr) | $40.97($40.97/hr) | Rent |
8x NVIDIA H100 SXM5 (80GB)Popular Hopper•NVLink-4 (900 GB/s) + Quantum-2 IB | Lambda Labs | 640 GB(8x 80GB) | $18.30($18.30/hr) | $23.92($23.92/hr) | Rent |
8x NVIDIA H100 SXM5 (80GB) Hopper•NVLink-4 (900 GB/s) + 3.2Tbps InfiniBand | RunPod | 640 GB(8x 80GB) | $19.80($19.80/hr) | $26.32($26.32/hr) | Rent |
8x NVIDIA H200 SXM5 (141GB) Hopper HBM3e•NVLink-4 (900 GB/s) + Quantum-2 IB | Lambda Labs | 1128 GB(8x 141GB) | $27.50($27.50/hr) | $34.32($34.32/hr) | Rent |
8x NVIDIA H100 SXM5 (80GB) Hopper•NVLink-4 (900 GB/s) + GPUDirect-TCPX | GCP (a3-highgpu-8g) | 640 GB(8x 80GB) | $32.00($32.00/hr) | $87.05($87.05/hr) | Rent |
8x NVIDIA H100 SXM5 (80GB) Hopper•NVLink-4 (900 GB/s) + 3.2Tbps EFA | AWS (EC2 p5.48xlarge) | 640 GB(8x 80GB) | $38.50($38.50/hr) | $98.32($98.32/hr) | Rent |
8x NVIDIA Blackwell B200 (192GB)Popular Blackwell•NVLink-5 (1.8 TB/s) + Quantum-X800 IB | Nebius / CoreWeave | 1536 GB(8x 192GB) | $44.00($44.00/hr) | $58.00($58.00/hr) | Rent |
Showing all 19 nodes • Scroll table vertically to view full hardware catalog
Prices updated February 2026Technical Knowledge Hub
Frequently Asked Questions & Transformer Math
Deep-dive technical answers on KV-cache calculation, CUDA Out-Of-Memory prevention, QLoRA fine-tuning benchmarks, and cloud GPU cost optimization.
Gemma 2 27B Architectural Specs & Memory Scaling
Detailed breakdown of transformer parameters, attention mechanisms, and KV-cache expansion.
Total Parameters27.2 Billion
Transformer Layers46 Layers
Attention Heads / GQA32 Q / 16 KV (2:1)
Max Native Context8,192 Tokens
Quantization Precision vs VRAM Footprint
| Precision / Quant Type | Bytes / Param | Model Weights VRAM | Compatible Single GPU |
|---|---|---|---|
| FP32 (32-bit Float) | 4 B | 101.33 GB | Requires Multi-GPU / A100 (80GB) |
| FP16 (16-bit Float) | 2 B | 50.66 GB | Requires Multi-GPU / A100 (80GB) |
| FP8 (8-bit Float) | 1 B | 25.33 GB | ✓ Fits on 1x RTX 5090 (32GB) |
| INT8 (8-bit Integer) | 1 B | 25.33 GB | ✓ Fits on 1x RTX 5090 (32GB) |
| INT4 / AWQ / GPTQ | 0.55 B | 13.93 GB | ✓ Fits on 1x RTX 4090 (24GB) |
| GGUF Q4_K_M | 0.58 B | 14.69 GB | ✓ Fits on 1x RTX 4090 (24GB) |
KV-Cache Memory Growth with Sequence Length
| Context Window (Tokens) | FP16 KV Cache (2 B) | FP8 KV Cache (1 B - vLLM) | VRAM Savings |
|---|---|---|---|
| 4,096 tokens | 1.44 GB | 0.72 GB | -50% reduction |
| 8,192 tokens | 2.88 GB | 1.44 GB | -50% reduction |
Compare Similar Model Calculators
Explore VRAM footprints for related open-weights architectures.
70.6B Params
Llama 3.3 70B Instruct
Meta's state-of-the-art dense 70B open-weights powerhouse with 128k context and 8:1 Grouped Query Attention (GQA).
Calculate VRAM→
8.03B Params
Llama 3.1 8B Instruct
The industry standard compact LLM for local reasoning, autonomous agents, and fast fine-tuning pipelines.
Calculate VRAM→
671B Params
DeepSeek R1 (671B MoE)
Frontier open reasoning model featuring 671B total parameters, 37B active per token, and Multi-Head Latent Attention (MLA).
Calculate VRAM→
671B Params
DeepSeek V3 (671B MoE)
Flagship 671B mixture-of-experts generalist model with state-of-the-art coding and multilingual performance.
Calculate VRAM→