Medgemma 27B Text IT GGUF — Hardware Requirements & GPU Compatibility
Vision
2.7K downloads
62 likesSpecifications
- Publisher
- Unsloth
- Family
- Gemma 2
- Parameters
- 27B
- Architecture
- Gemma3ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- License
- Other
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HuggingFace
How Much VRAM Does Medgemma 27B Text IT GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 13.1 GB | 99.2 GB | 11.47 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 13.5 GB | 99.5 GB | 11.81 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 14.8 GB | 100.8 GB | 13.16 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 17.9 GB | 103.9 GB | 16.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 20.9 GB | 106.9 GB | 19.24 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 23.9 GB | 110.0 GB | 22.27 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 28.7 GB | 114.7 GB | 27.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Medgemma 27B Text IT GGUF?
Q4_K_M · 17.9 GBMedgemma 27B Text IT GGUF (Q4_K_M) requires 17.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ GB is recommended. Using the full 131K context window can add up to 86.0 GB, bringing total usage to 103.9 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Medgemma 27B Text IT GGUF?
Q4_K_M · 17.9 GB21 devices with unified memory can run Medgemma 27B Text IT GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Medgemma 27B Text IT GGUF need?
Medgemma 27B Text IT GGUF requires 17.9 GB of VRAM at Q4_K_M, or 28.7 GB at Q8_0. Full 131K context adds up to 86.0 GB (103.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 27B × 4.8 bits ÷ 8 = 16.2 GB
KV Cache + Overhead ≈ 1.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 87.7 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M17.9 GBQ4_K_M + full context103.9 GB- Can NVIDIA GeForce RTX 4090 run Medgemma 27B Text IT GGUF?
Yes, at Q6_K (23.9 GB) or lower. Higher quantizations like Q8_0 (28.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Medgemma 27B Text IT GGUF?
For Medgemma 27B Text IT GGUF, Q4_K_M (17.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (20.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.1 GB.
VRAM requirement by quantization
IQ2_XXS9.1 GB~53%Q3_K_S13.5 GB~77%Q4_116.9 GB~88%Q4_K_M ★17.9 GB~89%Q5_K_S20.2 GB~92%Q8_028.7 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Medgemma 27B Text IT GGUF on a Mac?
Medgemma 27B Text IT GGUF requires at least 9.1 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 Medgemma 27B Text IT GGUF locally?
Yes — Medgemma 27B Text IT GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 17.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Medgemma 27B Text IT GGUF?
At Q4_K_M, Medgemma 27B Text IT GGUF can reach ~163 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~37 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 ÷ 17.9 × 0.55 = ~163 tok/s
Estimated speed at Q4_K_M (17.9 GB)
AMD Instinct MI300X~163 tok/sNVIDIA GeForce RTX 4090~37 tok/sNVIDIA H100 SXM~122 tok/sAMD Instinct MI250X~101 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Medgemma 27B Text IT GGUF?
At Q4_K_M, the download is about 16.20 GB. The full-precision Q8_0 version is 27.00 GB. The smallest option (IQ2_XXS) is 7.43 GB.
- Which GPUs can run Medgemma 27B Text IT GGUF?
6 consumer GPUs can run Medgemma 27B Text IT GGUF at Q4_K_M (17.9 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Medgemma 27B Text IT GGUF?
21 devices with unified memory can run Medgemma 27B Text IT GGUF at Q4_K_M (17.9 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.