Functiongemma 270M Ft Tiny Garden — Hardware Requirements & GPU Compatibility
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Functiongemma 270M Ft Tiny Garden is a 270M-parameter open language model from litert-community in the Gemma family. At BF16 it needs about 0.59 GB of VRAM — see which GPUs and Macs can run it below.
20 downloads
9 likesSpecifications
- Publisher
- litert-community
- Family
- Gemma
- Parameters
- 270M
- Release Date
- 2026-02-25
- License
- Gemma Terms
Get Started
How Much VRAM Does Functiongemma 270M Ft Tiny Garden Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 0.6 GB | — | 0.54 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 Functiongemma 270M Ft Tiny Garden?
BF16 · 0.6 GBFunctiongemma 270M Ft Tiny Garden (BF16) requires 0.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Functiongemma 270M Ft Tiny Garden?
BF16 · 0.6 GB59 devices with unified memory can run Functiongemma 270M Ft Tiny Garden, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Functiongemma 270M Ft Tiny Garden need?
Functiongemma 270M Ft Tiny Garden requires 0.6 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 270M × 16 bits ÷ 8 = 0.5 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF160.6 GB- Can I run Functiongemma 270M Ft Tiny Garden on a Mac?
Functiongemma 270M Ft Tiny Garden requires at least 0.6 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 Functiongemma 270M Ft Tiny Garden locally?
Yes — Functiongemma 270M Ft Tiny Garden can run locally on consumer hardware. At BF16 quantization it needs 0.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Functiongemma 270M Ft Tiny Garden?
At BF16, Functiongemma 270M Ft Tiny Garden can reach ~8136 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1111 tok/s. 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 ÷ 0.6 × 0.65 = ~8814 tok/s
Estimated speed at BF16 (0.6 GB)
~8814 tok/s~1111 tok/s~8814 tok/s~8136 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Functiongemma 270M Ft Tiny Garden?
At BF16, the download is about 0.54 GB.
- Which GPUs can run Functiongemma 270M Ft Tiny Garden?
50 consumer GPUs can run Functiongemma 270M Ft Tiny Garden at BF16 (0.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Functiongemma 270M Ft Tiny Garden?
59 devices with unified memory can run Functiongemma 270M Ft Tiny Garden at BF16 (0.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.