Can I Run Google Gemma 4 26B MoE on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Google Gemma 4 26B MoE (26.4B) on NVIDIA GeForce RTX 3090 (24GB GDDR6X) (24 GB).
✓ Fits Comfortably in VRAM
Local 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 Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0
NVIDIA GeForce RTX 3090 (24GB GDDR6X)
The #1 budget 24GB card for local LLMs, running 14B–32B models and Q4 70B quantized
Cloud Instance Options for Google Gemma 4 26B MoE
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 | 52.8 GB | 3.09 GB | 58.49 GB (244%) | 0 t/s | OOM |
| Q8_08.5 bpw | 27.98 GB | 3.09 GB | 32.8 GB (137%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 21.65 GB | 3.09 GB | 26.25 GB (109%) | ~32 t/s | PARTIAL |
| Q5_K_M5.68 bpw | 18.74 GB | 3.09 GB | 23.24 GB (97%) | ~162 t/s | FITS |
| Q4_K_M4.8 bpw | 15.84 GB | 3.09 GB | 20.23 GB (84%) | ~185 t/s | FITS |
| FP44.25 bpw | 13.99 GB | 3.09 GB | 18.32 GB (76%) | ~203 t/s | FITS |
| IQ3_XS3.3 bpw | 10.82 GB | 3.09 GB | 15.04 GB (63%) | ~244 t/s | FITS |
Frequently Asked Questions: Google Gemma 4 26B MoE on NVIDIA GeForce RTX 3090 (24GB GDDR6X)
How much VRAM does Google Gemma 4 26B MoE need on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Google Gemma 4 26B MoE requires 20.23 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Google Gemma 4 26B MoE achieve on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?
Based on NVIDIA GeForce RTX 3090 (24GB GDDR6X)'s 936 GB/s memory bandwidth, expected decode speed is approximately 185 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.