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.
Gemma 4 19B runs on a 48 GB+ Mac or a workstation GPU
Best local pick → Mac Studio M5 Max (48 GB) · BF16 · 39 GB · ~37 tok/s
Macs from 48 GB (BF16 · ~37 tok/s)
Gemma Terms
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 4 19B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve 0xSero/Gemma-4-19B --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on A40 (48 GB) (9 GB headroom) · BF16
- Generation speed
- ~47 tok/s
- generation speed
- Cost per 1M output tokens
- $2.08
- per 1M output tokens
How Much VRAM Does Gemma 4 19B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 38.7 GB | 39.6 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 39 GB |
Selected BF16
Brain floating point 1639.0GBat 16K context
- Weights
- 38.0 GB
- KV cache
- 0.7 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 15.0 GB more
Which Hardware 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 8.7 GB, bringing total usage to 47.3 GB. No consumer GPU has enough memory. 67 devices with unified memory can run Gemma 4 19B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M5 Max (48 GB).
48 GB
48 GB tier →fits · 9.0 GB free · up to 256K ctx
2 Mac classes · 1 device family
M5 Max48 GB~37Simulate output on Mac Studio M5 Max (48 GB)M5 Pro48 GB~21Simulate output on Mac Mini M5 Pro (48 GB)Snapdragon X2 Elite Extreme (X2E-96-100)~16Simulate output on Snapdragon X2 Elite Extreme Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Alternatives to Gemma 4 19B
Gemma 4 family · 35 models →Similar models
1| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Internlm 20BInternLM | — | 45.3 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- 0xSero
- Family
- Gemma 4
- Parameters
- 19.0B
- Active parameters
- 4.4B per token
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-04-05
- License
- Gemma Terms
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 8.7 GB (47.3 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 9.3 GB (at full 262K context)
VRAM usage by quantization
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?
Yes — Mac Studio M5 Max (48 GB) and 55 other Macs can run Gemma 4 19B. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 38.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 4 19B locally?
Yes — Gemma 4 19B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 4 19B?
At BF16, Gemma 4 19B can reach ~127 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 8.9 GB active ÷ (8000 × 0.65) = 1.72 ms, plus 30 layers × 0.055 ms = 1.65 ms, so 1000 ÷ 3.37 ms = ~297 tok/s
Estimated speed at BF16 (38.7 GB)
Real-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?
67 devices with unified memory can run Gemma 4 19B at BF16 (38.7 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.