HARDWARE FIT CHECK • Google Gemma 4 26B MoE + NVIDIA RTX 4070 Ti Super (16GB)20.23 GB / 16 GB VRAM
Can I Run Google Gemma 4 26B MoE on NVIDIA RTX 4070 Ti Super (16GB)?
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 RTX 4070 Ti Super (16GB) (16 GB).
⚠️ Exceeds VRAM — Requires Partial CPU/RAM Offload (Slow)20.23 GB (126%)~30 t/s
Google Gemma 4 26B MoE on NVIDIA RTX 4070 Ti Super (16GB)
⚠️ Partial Offload — Slow CPU/RAM Spillover
⚡ PARTIAL OFFLOAD
Required VRAM
20.23 GB
126% of hardware capacity (16 GB)Est. Decode Speed
~30 tok/s
4.2B active params @ 672 GB/sAvailable Headroom
-4.23 GB
Needs +4.23 GB more VRAMMemory Allocation & Headroom
20.23 GB allocated of 16 GB capacity
126% utilized
Weights: 15.84 GB
KV: (32K): 3.09 GB
Overhead: 1.3 GB
Deficit: -4.23 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~30 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA RTX 4070 Ti Super (16GB) (672 GB/s). Prompt prefill / TTFT (~435 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 gemma4:26b⚠️ 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 4 26B MoE @ 20.23 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 | 52.8 GB | 3.09 GB | 58.49 GB (366%) | 0 t/s | OOM |
| Q8_08.5 bpw | 27.98 GB | 3.09 GB | 32.8 GB (205%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 21.65 GB | 3.09 GB | 26.25 GB (164%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 18.74 GB | 3.09 GB | 23.24 GB (145%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 15.84 GB | 3.09 GB | 20.23 GB (126%) | ~30 t/s | PARTIAL |
| FP44.25 bpw | 13.99 GB | 3.09 GB | 18.32 GB (115%) | ~33 t/s | PARTIAL |
| IQ3_XS3.3 bpw | 10.82 GB | 3.09 GB | 15.04 GB (94%) | ~181 t/s | FITS |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesConsumer GPU16 GB VRAM
NVIDIA RTX 4070 Ti Super (16GB)
Mid-range 16GB gaming + local AI daily driver
672 GB/s285W TDP
$799Amazon Prime →
Consumer GPU32 GB VRAM
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
1792 GB/s575W TDP
$1,999Amazon Prime →
Consumer GPU24 GB VRAM
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon Prime →
Multi-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 →
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