HARDWARE FIT CHECK โข Google Gemma 4 E4B (Edge 4B) + 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)4.24 GB / 48 GB VRAM
Can I Run Google Gemma 4 E4B (Edge 4B) on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Google Gemma 4 E4B (Edge 4B) (4.1B) on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) (48 GB).
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Runs 100% in GPU VRAM (Optimal Headroom)4.24 GB (9%)~215 t/s
Google Gemma 4 E4B (Edge 4B) on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
โ Fits Comfortably in VRAM
โ FITS IN VRAM
Required VRAM
4.24 GB
9% of hardware capacity (48 GB)Est. Decode Speed
~215 tok/s
Dense model @ 936 GB/sAvailable Headroom
+43.76 GB
Safe for OS display & full contextMemory Allocation & Headroom
4.24 GB allocated of 48 GB capacity
9% utilized
Weights: 2.46 GB
KV: (32K): 0.94 GB
Overhead: 0.84 GB
Free Headroom: +43.76 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~215 tok/s) is memory-bandwidth bound at batch size = 1 on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) (936 GB/s). Prompt prefill / TTFT (~3,118 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 (21%) | ~69 t/s | FITS |
| Q8_08.5 bpw | 4.35 GB | 0.94 GB | 6.19 GB (13%) | ~127 t/s | FITS |
| Q6_K6.56 bpw | 3.36 GB | 0.94 GB | 5.17 GB (11%) | ~161 t/s | FITS |
| Q5_K_M5.68 bpw | 2.91 GB | 0.94 GB | 4.7 GB (10%) | ~185 t/s | FITS |
| Q4_K_M4.8 bpw | 2.46 GB | 0.94 GB | 4.24 GB (9%) | ~215 t/s | FITS |
| FP44.25 bpw | 2.17 GB | 0.94 GB | 3.94 GB (8%) | ~241 t/s | FITS |
| IQ3_XS3.3 bpw | 1.68 GB | 0.94 GB | 3.43 GB (7%) | ~303 t/s | FITS |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesMulti-GPU Rig48 GB VRAM
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
936 GB/s700W TDP
$1,550Amazon Prime โ
Consumer GPU32 GB VRAM
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27Bโ35B models & Blackwell FP4 quantization
1792 GB/s575W TDP
$1,999Amazon Prime โ
Consumer GPU24 GB VRAM
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14Bโ32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon Prime โ
Consumer GPU16 GB VRAM
NVIDIA GeForce RTX 5080 (16GB GDDR7)
Blazing fast 8Bโ14B coding models with FP4/Q6_K precision
960 GB/s360W TDP
$999Amazon Prime โ
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