Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit — Hardware Requirements & GPU Compatibility
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Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit is a 4B-parameter open language model from deadbydawn101 in the Gemma family. It supports a context window of up to 131,072 tokens. At BF16 it needs about 8.52 GB of VRAM — see which GPUs and Macs can run it below.
6.2K downloads
27 likesSpecifications
- Publisher
- deadbydawn101
- Family
- Gemma
- Parameters
- 4B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-04-15
- License
- Gemma Terms
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How Much VRAM Does Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 8.5 GB | 22.4 GB | 8.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit?
BF16 · 8.5 GBGemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit (BF16) requires 8.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. Using the full 131K context window can add up to 13.9 GB, bringing total usage to 22.4 GB. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit?
BF16 · 8.5 GB27 devices with unified memory can run Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit need?
Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit requires 8.5 GB of VRAM at BF16. Full 131K context adds up to 13.9 GB (22.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4B × 16 bits ÷ 8 = 8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 14.4 GB (at full 131K context)
VRAM usage by quantization
BF168.5 GBBF16 + full context22.4 GB- Can I run Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit on a Mac?
Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit requires at least 8.5 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit locally?
Yes — Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit can run locally on consumer hardware. At BF16 quantization it needs 8.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit?
At BF16, Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit can reach ~342 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~77 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 8.5 × 0.55 = ~342 tok/s
Estimated speed at BF16 (8.5 GB)
~342 tok/s~77 tok/s~256 tok/s~212 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit?
At BF16, the download is about 8.00 GB.
- Which GPUs can run Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit?
28 consumer GPUs can run Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit at BF16 (8.5 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit?
27 devices with unified memory can run Gemma 4 E4B Agentic Opus Reasoning GeminiCLI MLX 4bit at BF16 (8.5 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.