Gemma 4 Hardware Requirements: From Smartphones to H100, a Complete Guide

TL;DR: Gemma 4 E2B runs on phones with just 1.5GB of memory. E4B needs a laptop with 6GB VRAM. 26B MoE requires 16-18GB VRAM at 4-bit quantization (RTX 4090 fits perfectly). 31B Dense at full precision needs 62GB (one H100). Pick the right quantization level, and consumer hardware can handle surprisingly large models.

"Can my computer run Gemma 4?" That's been the most common question I've heard since Google released Gemma 4 on April 2, 2026.

The answer depends on three things: which model variant you want to run, what quantization precision you'll use, and how much inference quality you're willing to sacrifice. This article lays out every combination of those three variables so you can make informed hardware decisions — no guesswork, no regrets.

Not sure which Gemma 4 model fits your use case? Book a free AI consultation and let our team recommend the optimal hardware configuration for your scenario.

For a complete overview of Gemma 4's capabilities and architecture, see the Gemma 4 Complete Guide.

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Memory Requirements Quick Reference for All Four Models

Let's start with the bottom line. Here are the memory requirements for all four Gemma 4 models across three common quantization levels, based on community benchmarks and Unsloth's official GGUF releases:

Model	4-bit (Q4_K_M)	8-bit (Q8_0)	BF16 (Full Precision)
E2B (2.3B)	~1.5 GB	~2.5 GB	~4.6 GB
E4B (4.3B)	~3 GB	~4.5 GB	~8.6 GB
26B MoE (A4B)	~16 GB	~27 GB	~50 GB
31B Dense	~18 GB	~33 GB	~62 GB

A few key observations:

E2B is a true pocket model. At 4-bit quantization it needs just 1.5GB, and LiteRT supports 2-bit quantization that pushes it below 1GB. A mid-range Android phone handles it comfortably.

26B MoE and 31B Dense have surprisingly similar VRAM needs at 4-bit. Despite the 26B having fewer total parameters, MoE architecture requires loading all expert weights into memory even though only 3.8B parameters activate per token. At 4-bit quantization, the gap is only about 2GB.

The difference between BF16 and 4-bit is 3-4x in memory. Quantization is the key technology that makes large models fit on consumer hardware. But it comes at a cost — we'll dig into quality impact later.

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Which Models Can Consumer Hardware Run?

This is the question everyone asks: what can you run without server-grade hardware?

NVIDIA GeForce GPUs

GPU	VRAM	Largest Runnable Model	Speed Reference
RTX 3060	12 GB	E4B (Q8), 26B MoE (Q4, tight)	E4B Q8: ~35 tok/s
RTX 3090	24 GB	26B MoE (Q4, comfortable), 31B Dense (Q4, tight)	26B Q4: ~42 tok/s
RTX 4060 Ti	16 GB	26B MoE (Q4, just fits)	26B Q4: ~38 tok/s
RTX 4090	24 GB	26B MoE (Q4, comfortable), 31B Dense (Q4, tight)	26B Q4: ~52 tok/s
RTX 5060 Ti	16 GB	26B MoE (Q4)	26B Q4: ~45 tok/s
RTX 5090	32 GB	31B Dense (Q4, comfortable)	31B Q4: ~60 tok/s

The RTX 5060 Ti is the value champion of 2026 — at $429, it runs the 26B MoE at Q4 quantization. Two years ago, that kind of capability at that price was unthinkable.

Both the RTX 4090 and RTX 3090 have 24GB VRAM, which handles the 26B MoE Q4 (~16GB) with room to spare for KV cache. But running the 31B Dense Q4 (~18GB) leaves minimal headroom for long contexts.

Apple Silicon Macs

Machine	Unified Memory	Largest Runnable Model	Speed Reference
M1/M2 (8GB)	8 GB	E4B (Q4)	E4B Q4: ~20 tok/s
M1/M2 (16GB)	16 GB	26B MoE (Q4, tight)	26B Q4: ~15 tok/s
M3/M4 (24GB)	24 GB	26B MoE (Q4, comfortable)	26B Q4: ~25 tok/s
M4 Pro (48GB)	48 GB	31B Dense (Q8)	31B Q8: ~20 tok/s
M4 Max (128GB)	128 GB	31B Dense (BF16)	31B BF16: ~15 tok/s

The Mac advantage is unified memory architecture — CPU and GPU share the same memory pool, unlike NVIDIA's separate VRAM. The M4 Max with 128GB can even run the 31B Dense at full BF16 precision without quantization.

The disadvantage is memory bandwidth. The M4 Max's 546 GB/s sounds impressive, but it's an order of magnitude below the H100's 3.35 TB/s. So while the Mac can load the full model, inference speed will be noticeably slower.

Use the MLX framework for Mac inference — it delivers 30-50% higher throughput than llama.cpp on Apple Silicon.

Need help evaluating what your existing hardware can run? Contact our technical team for free hardware configuration advice.

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Smartphones and Tablets: E2B and E4B Edge Deployment

The most impressive thing about Gemma 4 is that E2B genuinely runs well on phones. Not in a "technically possible but painfully slow" way — it's actually fast enough for real-time conversation.

Android Deployment

Google launched the AI Edge Gallery app on the same day as Gemma 4's release. You can download E2B and E4B models directly from the app with zero technical setup required.

For developers seeking deeper integration, the LiteRT-LM framework is the production path. It supports 2-bit and 4-bit quantization plus memory-mapped per-layer embeddings, allowing E2B to run with under 1.5GB memory on some devices. A flagship phone with Snapdragon 8 Gen 4 can achieve 15-20 tok/s on E2B.

iOS Deployment

Good news: AI Edge Gallery now supports iOS as well. iPhone 15 Pro and later (6GB RAM) can run E2B Q4, while iPhone 16 Pro (8GB RAM) handles E4B Q4.

Practical Use Cases

• E2B is ideal for: voice assistants, real-time translation, simple Q&A, IoT device control
• E4B is ideal for: document summarization, advanced conversation, image understanding, offline search

One important caveat: running AI models on phones generates significant heat. Most phones will start thermal throttling after 5+ minutes of continuous inference. Factor in thermal testing and throttling strategies for any production deployment.

For complete local deployment tutorials, see the Gemma 4 Local Deployment Guide.

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Server-Grade Hardware: Configuring for 26B MoE and 31B Dense

For production deployments, consumer hardware won't cut it. Here are server GPU configuration recommendations:

Single-GPU Deployment Options

GPU	VRAM	Best For	Monthly Cost Reference
NVIDIA L40S	48 GB	26B MoE (Q8), 31B Dense (Q4)	~$655/mo (INT8)
NVIDIA A100	80 GB	31B Dense (BF16)	~$0.61/hr (spot)
NVIDIA H100	80 GB	31B Dense (BF16), high concurrency	~$0.99/hr (spot)

The L40S is the sweet spot for small to mid-size teams. Its 48GB VRAM comfortably runs the 26B MoE at Q8 (~27GB) or the 31B Dense at Q4 (~18GB) with generous KV cache headroom. At roughly $655/month, it costs nearly 50% less than an H100.

The H100 is the standard for high-concurrency production. Its 3.35 TB/s memory bandwidth delivers far higher throughput than the A100 (2 TB/s) or L40S (864 GB/s) when handling multiple concurrent requests. If you need to serve 50+ simultaneous users, H100 with BF16 is the most reliable option.

The A100 is the value compromise. It matches the H100's 80GB VRAM at a lower price point, but falls behind in bandwidth and Tensor Core performance. Latency increases under high concurrency.

Multi-GPU Configurations

If you need to run the 31B Dense BF16 (62GB) but only have A100 40GB machines, tensor parallelism across two GPUs works. Both vLLM and TensorRT-LLM support this, though it adds roughly 10-15% latency overhead.

My Recommendations

Scenario	Recommended Setup	Rationale
MVP / Low Traffic	L40S + 26B MoE Q8	Low cost, high quality
Production / Medium Traffic	A100 80GB + 31B Dense BF16	Best quality, reasonable cost
High Concurrency / Strict SLA	H100 + 31B Dense BF16	Maximum throughput

Need a server-grade Gemma 4 deployment plan? Book a technical consultation — we provide end-to-end support from hardware selection to production launch.

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Quantization Quality Impact: Is 4-Bit Good Enough?

This is the question everyone asks: how much quality do you actually lose with quantization?

Quantization Levels and Quality Loss

Gemma 4's architecture was designed with quantization-friendliness in mind, making it more resilient to quantization than many comparable models. Based on community benchmarks:

Quantization Level	Memory Savings	Quality Loss	Best For
BF16 (None)	Baseline	None	Research, quality-sensitive production
Q8_0 (8-bit)	~50%	Barely noticeable (<1%)	Production gold standard
Q6_K (6-bit)	~62%	Minimal (~1-2%)	Best quality-efficiency balance
Q4_K_M (4-bit)	~75%	Noticeable (~3-5%)	Best option when VRAM is limited
Q2_K (2-bit)	~87%	Significant (~10-15%)	Edge devices only, non-critical tasks

When Is 4-Bit Good Enough?

Good enough for:

• General conversation and Q&A
• Code generation and debugging
• Document summarization
• Creative writing

Not ideal for:

• Complex mathematical reasoning (AIME scores drop 5-8% at Q4)
• Multi-step logical reasoning
• Tasks requiring precise numerical outputs
• Post-fine-tuning deployment in specialized domains

NVIDIA NVFP4: A Game Changer

NVIDIA's NVFP4 format on Blackwell architecture deserves special attention. It's a hardware-native 4-bit floating-point format that preserves significantly more precision than traditional Q4 integer quantization. On an RTX 5090 or Blackwell server GPU, NVFP4 delivers near-8-bit quality at 4-bit memory footprint.

My Quantization Recommendations

• Ample VRAM (48GB+) → Q8 or BF16
• Limited VRAM (16-24GB) → Q4_K_M, the reliable workhorse
• Edge devices (4-8GB) → Q4 or lower, paired with LiteRT

Want to learn how fine-tuning can compensate for quantization loss? See the Gemma 4 Fine-Tuning Guide.

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Frequently Asked Questions

Can my RTX 3060 12GB run Gemma 4?

Yes, but it depends on the model. All quantization levels of E2B and E4B work fine. The 26B MoE Q4 requires ~16GB, which exceeds the 3060's 12GB VRAM. The largest model you can run is E4B at full BF16 precision (~8.6GB), which is already a highly capable model.

How does the 26B MoE perform on a Mac Mini M4 (24GB)?

It runs well. The 26B MoE Q4 needs ~16GB, leaving 8GB of the 24GB unified memory for system overhead and KV cache. Using MLX, you can expect roughly 25 tok/s generation speed — perfectly usable for everyday conversation and development assistance. The only caveat: very long contexts (>32K tokens) may cause out-of-memory issues.

Can you fine-tune quantized models?

Yes, though the recommended approach is QLoRA (Quantized LoRA). Load the 4-bit quantized base model, then train LoRA adapters at BF16 precision. This requires only an additional 2-4GB VRAM for high-quality fine-tuning on top of the quantized model. The Unsloth framework has particularly strong QLoRA support for Gemma 4.

How much difference is there between H100 and A100 for Gemma 4 31B?

The main gap is in throughput and concurrency. Single-request latency differs by roughly 20-30%, but at 32 concurrent requests, the H100 delivers 1.5-2x the throughput of an A100. For development and testing, the A100 is perfectly adequate. For production serving multiple users, the H100 investment pays for itself.