Gemma 4 19B — Hardware Requirements & GPU Compatibility
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Gemma 4 19B is a 19.0B-parameter open language model from 0xSero in the Gemma family. It supports a context window of up to 262,144 tokens. At BF16 it needs about 38.69 GB of VRAM — see which GPUs and Macs can run it below.
415 downloads
19 likesSpecifications
- Publisher
- 0xSero
- Family
- Gemma
- Parameters
- 19.0B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-05-30
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Gemma 4 19B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 38.7 GB | 82.6 GB | 38.05 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Gemma 4 19B?
BF16 · 38.7 GBGemma 4 19B (BF16) requires 38.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 51+ GB is recommended. Using the full 262K context window can add up to 44.0 GB, bringing total usage to 82.6 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Gemma 4 19B?
BF16 · 38.7 GB11 devices with unified memory can run Gemma 4 19B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Pro 16" M4 Max (48 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 4 19B need?
Gemma 4 19B requires 38.7 GB of VRAM at BF16. Full 262K context adds up to 44.0 GB (82.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 19.0B × 16 bits ÷ 8 = 38 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 44.6 GB (at full 262K context)
VRAM usage by quantization
BF1638.7 GBBF16 + full context82.6 GB- Can NVIDIA GeForce RTX 5090 run Gemma 4 19B?
No — Gemma 4 19B requires at least 38.7 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Gemma 4 19B on a Mac?
Gemma 4 19B requires at least 38.7 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 19B locally?
Yes — Gemma 4 19B can run locally on consumer hardware. At BF16 quantization it needs 38.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 19B?
At BF16, Gemma 4 19B can reach ~75 tok/s on AMD Instinct MI300X. 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 ÷ 38.7 × 0.55 = ~75 tok/s
Estimated speed at BF16 (38.7 GB)
~75 tok/s~56 tok/s~47 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 19B?
At BF16, the download is about 38.05 GB.
- Which GPUs can run Gemma 4 19B?
No single consumer GPU has enough VRAM to run Gemma 4 19B at BF16 (38.7 GB). Multi-GPU or professional hardware is required.
- Which devices can run Gemma 4 19B?
11 devices with unified memory can run Gemma 4 19B at BF16 (38.7 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.