The gemma 4 vs llama 4 debate landed on developer forums in April 2026 with unusual intensity — two major open-weight releases within days of each other, both multimodal, both targeting the same developer audience. But when you take the comparison off the cloud benchmark leaderboards and down to your local machine, the picture shifts dramatically. Gemma 4's smallest model runs on 6 GB of VRAM; Llama 4 Scout needs 24 GB minimum even with aggressive quantization. This guide cuts through the noise with VRAM tables, benchmark data, and a use-case decision guide so you can pick the right model for your hardware today.

What Are Gemma 4 and Llama 4?

Both are open-weight, multimodal model families released in Q2 2026. Both support text and vision inputs. Both work with Ollama. Beyond that, their architecture choices diverge sharply — and that divergence is what determines whether your local machine can run them.

Gemma 4: Four Sizes, Every Hardware Tier

Google DeepMind released Gemma 4 on April 2, 2026, shipping four distinct model sizes under Apache 2.0:

• E2B (2B parameters) — edge-optimized, runs on phones, Raspberry Pi, and Jetson Orin Nano. Context: 128K tokens.
• E4B (4B parameters) — the sweet spot for most developers. Runs on integrated graphics or any 8 GB+ GPU. Context: 128K tokens.
• 26B A4B (MoE) — 26 billion total parameters but only 3.8 billion active per inference via Mixture-of-Experts routing. Near-27B quality at E4B-class VRAM cost during inference.
• 31B Dense — the flagship quality model for workstations and servers.

The E4B variant is the headline story. It beats Gemma 3 27B across every benchmark despite having a fraction of the parameters, and it fits on hardware most developers already own. For a deeper dive into the full Gemma 4 lineup and what changed architecturally, see our Gemma 4 vs Gemma 3 breakdown.

Llama 4: MoE Architecture and the VRAM Trap

Meta released Llama 4 in two locally-runnable variants (a third, Behemoth, is cloud-only):

• Scout — 109 billion total parameters, 17 billion active per token via MoE. Context: 10 million tokens. This is the model you would actually attempt to run locally.
• Maverick — 400 billion total parameters, 17 billion active per token. Requires a multi-GPU server or a maxed-out Mac Studio. Not realistically local for most developers.

The MoE VRAM trap: Mixture-of-Experts only saves compute during inference — not VRAM. You still need to load all 109B parameters into memory to run Scout. That means even though Scout only thinks with 17B parameters at once, it occupies memory proportional to its full 109B weight set.

Llama 4 supports images, video frames, and audio inputs natively across both Scout and Maverick. Gemma 4's E2B and E4B models also support audio, putting Gemma 4 ahead at the edge tier where Llama 4 simply cannot operate.

Hardware Requirements: What Can You Actually Run Locally?

This is where the Gemma 4 vs Llama 4 comparison becomes decisive at the consumer tier.

• Gemma 4 E2B — 4 GB VRAM at 4-bit, 5–8 GB at 8-bit, 10 GB FP16. Minimum: RTX 3060 / M1 8 GB
• Gemma 4 E4B — 6 GB at 4-bit, 9–12 GB at 8-bit, 16 GB FP16. Minimum: RTX 3060 12 GB / M2 16 GB
• Gemma 4 26B A4B — 16–18 GB at 4-bit, 28–30 GB at 8-bit, 52 GB FP16. Minimum: RTX 4090 / M2 Max 32 GB
• Gemma 4 31B — 17–20 GB at 4-bit, 34–38 GB at 8-bit, 62 GB FP16. Minimum: RTX 4090 / M2 Max 32 GB
• Llama 4 Scout (1.78-bit) — ~24 GB minimum, ~55 GB at Q4, ~200+ GB FP16. Minimum: RTX 3090 24 GB
• Llama 4 Maverick (1.78-bit) — ~100 GB minimum. Multi-GPU server only.

The practical takeaway: Gemma 4 E4B at 4-bit quantization fits on a 6 GB GPU — an RTX 3060 or even integrated Apple Silicon. Llama 4 Scout, even with the aggressive 1.78-bit Unsloth quantization, requires a 24 GB GPU at minimum and runs at approximately 20 tokens per second. Developers on RTX 4090s (24 GB VRAM) face a real choice: run Gemma 4 31B at comfortable 4-bit quantization, or squeeze Llama 4 Scout at an extreme quant level that degrades output quality.

Context window: Llama 4 Scout's 10 million token context is a genuine standout — no other locally-runnable model comes close. Gemma 4 31B tops out at 256K tokens, which is sufficient for most RAG and coding workflows, but if you need to load an entire large codebase or multi-volume document set into a single prompt, Scout has no peer at the local tier. This is the one area where Llama 4 wins clearly.

Benchmark Showdown: Coding, Reasoning, and Instruction Following

Raw benchmark numbers favor Gemma 4 across coding and reasoning tasks — and the margin on hard problems is not close.

• AIME 2026 (math competition): Gemma 4 31B: 89.2% | Gemma 4 E4B: 69.4% | Llama 4 Scout: not confirmed at press time
• LiveCodeBench v6 (coding): Gemma 4 31B: 80.0% | Gemma 4 E4B: 42.5% | Llama 4 Scout: below Gemma 4 31B per independent evals
• GPQA Diamond (science reasoning): Gemma 4 31B: 84.3% | Gemma 4 E4B: ~52% | Llama 4 Scout: below Gemma 4 31B per independent evals
• MMLU Pro: Gemma 4 31B: 85.2% | Gemma 4 E4B: 52.0% | Llama 4 Scout: comparable to Gemma 4 26B
• Codeforces ELO: Gemma 4 31B: 2150 | Llama 4 Scout: below 2000 per community benchmarks

Gemma 4 31B's jump from Gemma 3's Codeforces ELO of 110 to 2150 is the headline number for developers using LLMs as coding assistants. That is not a marginal improvement — it represents a qualitatively different class of programming capability. Llama 4 Scout performs respectably on general instruction-following but does not match Gemma 4 31B on hard reasoning or competitive coding tasks according to independent evaluations.

For general-purpose instruction following and creative tasks, Llama 4 Scout closes the gap. But for developers primarily using local LLMs for writing, debugging, or understanding code, Gemma 4 wins. Even Gemma 4 E4B — the model that fits on a 6 GB GPU — beats Gemma 3 27B on math and agentic tasks. To see how Gemma 4 compares across the full open-source landscape, see our Gemma 4 vs Qwen 3 comparison and Gemma 4 vs DeepSeek V3 analysis.

Licensing: What Developers Need to Know

Both models are open-weight, but their terms differ in commercially relevant ways.

Gemma 4 uses Apache 2.0 — the most permissive tier for an open-weight model. You can use it in commercial products, modify the weights, redistribute derivatives, and integrate it into any application regardless of scale. No revenue restrictions, no usage caps, no special approval required.

Llama 4 uses Meta's Llama 4 Community License. For most developers building products with fewer than 700 million monthly active users, this is effectively permissive — commercial use is allowed. However, the 700M MAU threshold and requirements to attribute Meta and comply with Meta's acceptable use policy introduce legal surface area that Apache 2.0 does not. Enterprise legal teams sometimes flag Llama licenses for review; Apache 2.0 typically clears compliance without escalation.

For solo developers and small teams: both licenses are practically workable. For enterprises or developers building platforms at scale, Gemma 4's Apache 2.0 is the simpler choice.

Setup, Tooling, and Community

Both models integrate with the same local inference stack. Ollama is the fastest path to running either:

# Gemma 4
ollama pull gemma4:4b
ollama pull gemma4:27b
ollama run gemma4:4b

# Llama 4
ollama pull llama4:scout
ollama run llama4:scout

LM Studio supports both model families through its model browser. Both expose OpenAI-compatible REST APIs when running via Ollama, which means any code already talking to OpenAI's API can switch to a local model with a one-line endpoint change.

Gemma 4 had broader quantization variant availability on HuggingFace at launch, with community GGUF quants for all four model sizes available within hours of release. Llama 4 Scout's larger total parameter count means GGUF conversion takes longer and 1.78-bit quants require Unsloth's specialized pipeline. The Gemma 4 community quant ecosystem is currently more mature for prosumer hardware. For a full walkthrough of the Gemma 4 local setup process, see our step-by-step Gemma 4 Ollama setup guide.

Which Should You Run? Decision Guide

• GPU with <16 GB VRAM (RTX 3060, 3070, 4060): Gemma 4 E4B — Scout won't fit. E4B at 6 GB VRAM delivers serious performance.
• GPU with 16–23 GB VRAM (RTX 3090, 4080): Gemma 4 26B A4B — 16–18 GB at 4-bit with better benchmark scores than Scout at comparable VRAM.
• GPU with 24 GB VRAM (RTX 3090/4090): Gemma 4 31B (default) or Llama 4 Scout — Gemma 4 31B fits at 20 GB 4-bit and outperforms Scout on coding/math. Pick Scout only if you need the 10M context window.
• Apple Silicon 32 GB+ (M2 Max/Ultra, M3 Max): Gemma 4 31B — fits comfortably with strong throughput on Apple GPU's unified memory architecture.
• Coding assistant (daily driver): Gemma 4 — Codeforces ELO 2150 at 31B. Even E4B outperforms older large models.
• Long-document RAG / large codebase ingestion: Llama 4 Scout — 10M token context is unmatched. The one scenario where Scout wins clearly.
• Edge / mobile / embedded deployment: Gemma 4 E2B — runs on phones and Raspberry Pi. Llama 4 has no equivalent at this tier.
• Agentic workflows and tool use: Gemma 4 — agentic benchmark scores jumped from 6% (Gemma 3) to 86% (E4B).
• Commercial product, enterprise compliance: Gemma 4 — Apache 2.0 clears legal review without scrutiny.

The verdict: Gemma 4 wins for local deployment at almost every hardware tier. It covers the full range from 6 GB VRAM to 32 GB workstations, outperforms Llama 4 Scout on coding and reasoning benchmarks, and ships with the most permissive open-source license available. Llama 4 Scout earns its place in one specific scenario: when your workflow genuinely requires a 10 million token context window and you have the 24 GB VRAM to run it.

If you are starting fresh and want the fastest path to a capable local model today, run ollama pull gemma4:4b and be up in under five minutes.