MODEL ARCHITECTURE SPEC โข GOOGLE4.1B Dense
Google Gemma 4 E4B (Edge 4B) VRAM & inference speed
4.1B Total ParamsDense Architecture28 Layers โข 4 KV Heads16K Default Ctx (Max 64K)Ollama: gemma4:4b
Accurate memory requirements, KV cache footprint, and tokens/sec generation benchmarks for running Google Gemma 4 E4B (Edge 4B) across local GPUs, Apple Silicon Macs, and cloud instances.
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Runs 100% in GPU VRAM (Optimal Headroom)4.24 GB (35%)~87 t/s
Google Gemma 4 E4B (Edge 4B) on NVIDIA GeForce RTX 3060 (12GB)
โ Fits Comfortably in VRAM
โ FITS IN VRAM
Required VRAM
4.24 GB
35% of hardware capacity (12 GB)Est. Decode Speed
~87 tok/s
Dense model @ 360 GB/sAvailable Headroom
+7.76 GB
Safe for OS display & full contextMemory Allocation & Headroom
4.24 GB allocated of 12 GB capacity
35% utilized
Weights: 2.46 GB
KV: (32K): 0.94 GB
Overhead: 0.84 GB
Free Headroom: +7.76 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~87 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 3060 (12GB) (360 GB/s). Prompt prefill / TTFT (~1,262 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 gemma4:4bCloud Instance Options for Google Gemma 4 E4B (Edge 4B)
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) โ $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) โ $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)Quantization Precision Matrix
32K ctx โข KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 8.2 GB | 0.94 GB | 10.18 GB (85%) | ~28 t/s | FITS |
| Q8_08.5 bpw | 4.35 GB | 0.94 GB | 6.19 GB (52%) | ~51 t/s | FITS |
| Q6_K6.56 bpw | 3.36 GB | 0.94 GB | 5.17 GB (43%) | ~65 t/s | FITS |
| Q5_K_M5.68 bpw | 2.91 GB | 0.94 GB | 4.7 GB (39%) | ~74 t/s | FITS |
| Q4_K_M4.8 bpw | 2.46 GB | 0.94 GB | 4.24 GB (35%) | ~87 t/s | FITS |
| FP44.25 bpw | 2.17 GB | 0.94 GB | 3.94 GB (33%) | ~97 t/s | FITS |
| IQ3_XS3.3 bpw | 1.68 GB | 0.94 GB | 3.43 GB (29%) | ~122 t/s | FITS |
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Hardware Compatibility Matrix for Google Gemma 4 E4B (Edge 4B)
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 โข 1792 GB/s | 32 GB | 4.24 GB | +27.76 GB | ~452 tok/s | โ FITS | Test Fit โ |
| NVIDIA GeForce RTX 4090 (24GB GDDR6X) Consumer GPU โข 1008 GB/s | 24 GB | 4.24 GB | +19.76 GB | ~254 tok/s | โ FITS | Test Fit โ |
| 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) Multi-GPU Rig โข 936 GB/s | 48 GB | 4.24 GB | +43.76 GB | ~215 tok/s | โ FITS | Test Fit โ |
| NVIDIA GeForce RTX 5080 (16GB GDDR7) Consumer GPU โข 960 GB/s | 16 GB | 4.24 GB | +11.76 GB | ~242 tok/s | โ FITS | Test Fit โ |
| NVIDIA RTX 4070 Ti Super (16GB) Consumer GPU โข 672 GB/s | 16 GB | 4.24 GB | +11.76 GB | ~167 tok/s | โ FITS | Test Fit โ |
| NVIDIA GeForce RTX 3060 (12GB) Consumer GPU โข 360 GB/s | 12 GB | 4.24 GB | +7.76 GB | ~87 tok/s | โ FITS | Test Fit โ |
| Apple Mac Mini M4 Pro (64GB Unified Memory) Apple Silicon โข 273 GB/s | 54 GB | 4.24 GB | +49.76 GB | ~64 tok/s | โ FITS | Test Fit โ |
| Apple Mac Studio M4 Max (128GB Unified Memory) Apple Silicon โข 546 GB/s | 112 GB | 4.24 GB | +107.76 GB | ~130 tok/s | โ FITS | Test Fit โ |
| Apple Mac Studio M5 Ultra (256GB Unified Memory) Apple Silicon โข 890 GB/s | 236 GB | 4.24 GB | +231.76 GB | ~211 tok/s | โ FITS | Test Fit โ |
| AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB) Unified APU / Mini-PC โข 256 GB/s | 110 GB | 4.24 GB | +105.76 GB | ~53 tok/s | โ FITS | Test Fit โ |
| NVIDIA DGX Spark (128GB Grace-Blackwell Unified) AI Appliance โข 273 GB/s | 118 GB | 4.24 GB | +113.76 GB | ~68 tok/s | โ FITS | Test Fit โ |
| NVIDIA H100 SXM (80GB HBM3 Cloud GPU) Datacenter Cloud GPU โข 3350 GB/s | 80 GB | 4.24 GB | +75.76 GB | ~895 tok/s | โ FITS | Test Fit โ |