Functiongemma 270M Ft Mobile Actions — Hardware Requirements & GPU Compatibility
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Functiongemma 270M Ft Mobile Actions 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.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 32K)Best on 24 GB (BF16 · ~1111 tok/s · 32K)Macs from 8 GB (BF16 · ~122 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Functiongemma 270M Ft Mobile Actions
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve litert-community/functiongemma-270m-ft-mobile-actions --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 RTX 3060 12GB (10 GB headroom) · BF16
- Generation speed
- ~397 tok/s
- generation speed
- Cost per 1M output tokens
- $0.058
- per 1M output tokens
How Much VRAM Does Functiongemma 270M Ft Mobile Actions Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.6 GB | 2.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~1111 tok/s |
Selected BF16
Brain floating point 161.6GBat 16K context
- Weights
- 0.2 GB
- KV cache
- 1.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.4 GB free · ~1111 tok/s
≥50 tok/s needs ≥45 GB/s · RTX 5090 ~1974, RTX 3090 Ti ~1111
Which Hardware Can Run Functiongemma 270M Ft Mobile Actions?
BF16 · 0.6 GBFunctiongemma 270M Ft Mobile Actions (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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Functiongemma 270M Ft Mobile Actions, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.4 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~838Simulate output on RTX 3080RTX 2080 Ti~679Simulate output on RTX 2080 TiRTX 3070 Ti~670Simulate output on RTX 3070 TiM3*8 GB~122Simulate output on iMac 24" M3 (8 GB)Orin Nano~112Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~81Simulate output on iPhone 1720 GB
fits · 18.4 GB free · up to 32K ctx
1 GPU
RX 7900 XT~814Simulate output on RX 7900 XT32–40 GB
fits · 30.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1974Simulate output on RTX 5090M5 Max36 GB~546Simulate output on Mac Studio M5 Max (36 GB)M532 GB~182Simulate output on MacBook Air 13" M5 (32 GB)Orin~226Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~149Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Alternatives to Functiongemma 270M Ft Mobile Actions
Gemma family · 16 models →Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 3 270M ITGoogle | — | 1.2 GB | ~3640 | same size |
| SmolVLM2 256M Video InstructHugging Face | — | 0.6 GB | ~1310 | same size |
| SmolVLM 256M InstructHugging Face | — | 0.6 GB | ~1310 | same size |
| Vlt5 Base KeywordsVoicelab | — | 1.6 GB | ~1074 | same size |
Specifications
- Publisher
- litert-community
- Family
- Gemma
- Parameters
- 270M
- Release Date
- 2026-02-25
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Functiongemma 270M Ft Mobile Actions need?
Functiongemma 270M Ft Mobile Actions 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can I run Functiongemma 270M Ft Mobile Actions on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Functiongemma 270M Ft Mobile Actions. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 0.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Functiongemma 270M Ft Mobile Actions locally?
Yes — Functiongemma 270M Ft Mobile Actions can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Functiongemma 270M Ft Mobile Actions?
At BF16, Functiongemma 270M Ft Mobile Actions 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 ÷ 0.59 × 0.65 = ~8814 tok/s
Estimated speed at BF16 (0.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Functiongemma 270M Ft Mobile Actions?
At BF16, the download is about 0.54 GB.
Which GPUs can run Functiongemma 270M Ft Mobile Actions?
52 consumer GPUs can run Functiongemma 270M Ft Mobile Actions at BF16. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Functiongemma 270M Ft Mobile Actions?
163 devices with unified memory can run Functiongemma 270M Ft Mobile Actions at BF16 (0.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, 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.