Codegemma 2B — Hardware Requirements & GPU Compatibility
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Codegemma 2B is a 2.5B-parameter open language model from Google in the Gemma 2 family. At Q4_K_M it needs about 1.65 GB of VRAM — see which GPUs and Macs can run it below.
31.0K downloads
100 likesSpecifications
- Publisher
- Family
- Gemma 2
- Parameters
- 2.5B
- Release Date
- 2024-03-21
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Codegemma 2B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 1.2 GB | — | 1.07 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 1.3 GB | — | 1.22 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 1.6 GB | — | 1.50 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 2.0 GB | — | 1.79 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 2.3 GB | — | 2.07 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 2.8 GB | — | 2.51 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 5.5 GB | — | 5.01 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 Codegemma 2B?
Q4_K_M · 1.6 GBCodegemma 2B (Q4_K_M) requires 1.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ 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 Codegemma 2B?
Q4_K_M · 1.6 GB59 devices with unified memory can run Codegemma 2B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Codegemma 2B need?
Codegemma 2B requires 1.6 GB of VRAM at Q4_K_M, or 5.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.5B × 4.8 bits ÷ 8 = 1.5 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.6 GB- What's the best quantization for Codegemma 2B?
For Codegemma 2B, Q4_K_M (1.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.2 GB.
VRAM requirement by quantization
Q2_K1.2 GBQ4_K_M ★1.6 GBQ5_K_M2.0 GBQ6_K2.3 GBQ8_02.8 GBBF165.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Codegemma 2B on a Mac?
Codegemma 2B requires at least 1.2 GB at Q2_K, 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 Codegemma 2B locally?
Yes — Codegemma 2B can run locally on consumer hardware. At Q4_K_M quantization it needs 1.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Codegemma 2B?
At Q4_K_M, Codegemma 2B can reach ~2667 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~397 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 ÷ 1.6 × 0.65 = ~3152 tok/s
Estimated speed at Q4_K_M (1.6 GB)
~3152 tok/s~397 tok/s~3152 tok/s~2667 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Codegemma 2B?
At Q4_K_M, the download is about 1.50 GB. The full-precision BF16 version is 5.01 GB. The smallest option (Q2_K) is 1.07 GB.
- Which GPUs can run Codegemma 2B?
50 consumer GPUs can run Codegemma 2B at Q4_K_M (1.6 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 Codegemma 2B?
59 devices with unified memory can run Codegemma 2B at Q4_K_M (1.6 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.