ExtGemma4 40 5B — Hardware Requirements & GPU Compatibility
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ExtGemma4 40 5B is a 39.5B-parameter open language model from TOTORONG in the Gemma 4 family. It supports a context window of up to 262,144 tokens. At BF16 it needs about 80.94 GB of VRAM — see which GPUs and Macs can run it below.
1.4K downloads
4 likesSpecifications
- Publisher
- TOTORONG
- Family
- Gemma 4
- Parameters
- 39.5B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-07-12
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does ExtGemma4 40 5B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 80.9 GB | 296.3 GB | 78.94 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 ExtGemma4 40 5B?
BF16 · 80.9 GBExtGemma4 40 5B (BF16) requires 80.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 106+ GB is recommended. Using the full 262K context window can add up to 215.3 GB, bringing total usage to 296.3 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run ExtGemma4 40 5B?
BF16 · 80.9 GB18 devices with unified memory can run ExtGemma4 40 5B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, ASUS Ascent GX10.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does ExtGemma4 40 5B need?
ExtGemma4 40 5B requires 80.9 GB of VRAM at BF16. Full 262K context adds up to 215.3 GB (296.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 39.5B × 16 bits ÷ 8 = 78.9 GB
KV Cache + Overhead ≈ 2 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 217.4 GB (at full 262K context)
VRAM usage by quantization
BF1680.9 GBBF16 + full context296.3 GB- Can NVIDIA GeForce RTX 5090 run ExtGemma4 40 5B?
No — ExtGemma4 40 5B requires at least 80.9 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run ExtGemma4 40 5B on a Mac?
ExtGemma4 40 5B requires at least 80.9 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 ExtGemma4 40 5B locally?
Yes — ExtGemma4 40 5B can run locally on consumer hardware. At BF16 quantization it needs 80.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is ExtGemma4 40 5B?
At BF16, ExtGemma4 40 5B can reach ~54 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 ÷ 80.9 × 0.65 = ~64 tok/s
Estimated speed at BF16 (80.9 GB)
~64 tok/s~64 tok/s~54 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of ExtGemma4 40 5B?
At BF16, the download is about 78.94 GB.
- Which GPUs can run ExtGemma4 40 5B?
No single consumer GPU has enough VRAM to run ExtGemma4 40 5B at BF16 (80.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run ExtGemma4 40 5B?
19 devices with unified memory can run ExtGemma4 40 5B at BF16 (80.9 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.