HARDWARE FIT CHECK • Google Gemma 4 26B MoE + NVIDIA GeForce RTX 4060 Ti (16GB GDDR6)20.23 GB / 16 GB VRAM

Can I Run Google Gemma 4 26B MoE on NVIDIA GeForce RTX 4060 Ti (16GB GDDR6)?

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 4060 Ti (16GB GDDR6) (16 GB).

! PARTIAL OFFLOAD20.23 GB (126%)~13 t/s

Google Gemma 4 26B MoE on NVIDIA GeForce RTX 4060 Ti (16GB GDDR6)MoE (4.2B Active / 26.4B Total)

! Partial Offload — Slow CPU/RAM Spillover

! PARTIAL OFFLOAD

Required VRAM

20.23 GB

126% of hardware capacity (16 GB)

Est. Decode Speed

~13 tok/s

4.2B active params @ 288 GB/s

Available Headroom

-4.23 GB

Needs +4.23 GB more VRAM

Memory Allocation & Headroom

20.23 GB allocated of 16 GB capacity

126% utilized

Weights: 15.84 GBMoE (4.2B Active)

KV: (32K): 3.09 GB

Overhead: 1.3 GB

Deficit: -4.23 GB

Architecture & Throughput Transparency: Single-user decode speed (~13 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 4060 Ti (16GB GDDR6) (288 GB/s). Prompt prefill / TTFT (~189 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).

MoE Memory vs Speed Architecture: While decode throughput scales with the active parameter count (4.2B parameters streamed per token), all 26.4B parameters must be resident in VRAM so the dynamic router can route tokens to any expert layer.

Local Hardware Configurations & Pricing

Partial Offload: Dedicated 100% GPU Options

High-bandwidth VRAM specs and current market prices

Consumer GPU✓ Fits Model

32 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 GPU✓ Fits Model

24 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 Rig✓ Fits Model

48 GB VRAM

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

1648 GB/s700W TDP

$1,550Amazon Prime →

Consumer GPU

16 GB VRAM

NVIDIA GeForce RTX 4060 Ti (16GB GDDR6)

Lowest-cost 16GB VRAM GPU for running 8B–14B models and LoRA fine-tuning on a budget

288 GB/s165W TDP

$449Amazon Prime →

Out of VRAM? Don't downgrade model quality.

Out of VRAM

Spin 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)

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$0.38/hr (RTX 4090 Spot) – $2.39/hr (H100)

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Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090

$0.18/hr (RTX 3060) – $0.78/hr (2x RTX 3090)

Rent Spot GPU →

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$2.49/hr (H100) – $19.92/hr (8x H100)

Check H100 Availability →

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%)	~13 t/s	PARTIAL
FP44.25 bpw	13.99 GB	3.09 GB	18.32 GB (115%)	~14 t/s	PARTIAL
IQ3_XS3.3 bpw	10.82 GB	3.09 GB	15.04 GB (94%)	~75 t/s	FITS

FAQ & Reference

Frequently Asked Questions: Google Gemma 4 26B MoE on NVIDIA GeForce RTX 4060 Ti (16GB GDDR6)

How much VRAM does Google Gemma 4 26B MoE need on NVIDIA GeForce RTX 4060 Ti (16GB GDDR6)?

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 4060 Ti (16GB GDDR6)?

Based on NVIDIA GeForce RTX 4060 Ti (16GB GDDR6)'s 288 GB/s memory bandwidth, expected decode speed is approximately 13 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.