Gemma 4 19B — Hardware Requirements & GPU Compatibility
Chat
Gemma 4 19B is a 19.0B-parameter open language model from 0xSero in the Gemma 4 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 4
- Parameters
- 19.0B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-04-05
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Gemma 4 19B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 38.7 GB | 82.6 GB | 38.05 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
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 GB27 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
Related 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 ~114 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 38.7 × 0.65 = ~134 tok/s
Estimated speed at BF16 (38.7 GB)
~134 tok/s~134 tok/s~114 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?
27 devices with unified memory can run Gemma 4 19B at BF16 (38.7 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.