Functiongemma 270M IT — Hardware Requirements & GPU Compatibility
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Functiongemma 270M IT is a 268M-parameter open language model from Google in the Gemma 2 family. At Q4_K_M it needs about 0.18 GB of VRAM — see which GPUs and Macs can run it below.
214.7K downloads
1.0K likesSpecifications
- Publisher
- Family
- Gemma 2
- Parameters
- 268M
- Release Date
- 2026-01-14
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Functiongemma 270M IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.1 GB | — | 0.11 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.1 GB | — | 0.12 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.1 GB | — | 0.13 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.1 GB | — | 0.13 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 0.2 GB | — | 0.16 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.2 GB | — | 0.19 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.2 GB | — | 0.22 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.3 GB | — | 0.27 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Functiongemma 270M IT?
Q4_K_M · 0.2 GBFunctiongemma 270M IT (Q4_K_M) requires 0.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Functiongemma 270M IT?
Q4_K_M · 0.2 GB33 devices with unified memory can run Functiongemma 270M IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (6)
Frequently Asked Questions
- How much VRAM does Functiongemma 270M IT need?
Functiongemma 270M IT requires 0.2 GB of VRAM at Q4_K_M, or 0.3 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 268M × 4.8 bits ÷ 8 = 0.2 GB
VRAM usage by quantization
Q4_K_M0.2 GB- What's the best quantization for Functiongemma 270M IT?
For Functiongemma 270M IT, Q4_K_M (0.2 GB) offers the best balance of quality and VRAM usage. Q4_K_L (0.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.1 GB.
VRAM requirement by quantization
IQ2_XXS0.1 GBQ3_K_S0.1 GBIQ4_XS0.2 GBQ4_K_M ★0.2 GBQ5_K_S0.2 GBQ8_00.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Functiongemma 270M IT on a Mac?
Functiongemma 270M IT requires at least 0.1 GB at IQ2_XXS, 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 IT locally?
Yes — Functiongemma 270M IT can run locally on consumer hardware. At Q4_K_M quantization it needs 0.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Functiongemma 270M IT?
At Q4_K_M, Functiongemma 270M IT can reach ~16194 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~3640 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 0.2 × 0.55 = ~16194 tok/s
Estimated speed at Q4_K_M (0.2 GB)
~16194 tok/s~3640 tok/s~12104 tok/s~10012 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 IT?
At Q4_K_M, the download is about 0.16 GB. The full-precision Q8_0 version is 0.27 GB. The smallest option (IQ2_XXS) is 0.07 GB.
- Which GPUs can run Functiongemma 270M IT?
35 consumer GPUs can run Functiongemma 270M IT at Q4_K_M (0.2 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Functiongemma 270M IT?
33 devices with unified memory can run Functiongemma 270M IT at Q4_K_M (0.2 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.