Gemma 4 RUST CODER 12B — Hardware Requirements & GPU Compatibility
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Gemma 4 RUST CODER 12B is a 12B-parameter open language model from MassivDash in the Gemma 4 family. At Q4_K_M it needs about 7.92 GB of VRAM — see which GPUs and Macs can run it below.
2 likesSpecifications
- Publisher
- MassivDash
- Family
- Gemma 4
- Parameters
- 12B
- Release Date
- 2026-07-18
- License
- MIT
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HuggingFace
How Much VRAM Does Gemma 4 RUST CODER 12B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 5.6 GB | — | 5.10 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 6.4 GB | — | 5.85 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 7.9 GB | — | 7.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 9.4 GB | — | 8.55 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 10.9 GB | — | 9.90 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 13.2 GB | — | 12.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 4 RUST CODER 12B?
Q4_K_M · 7.9 GBGemma 4 RUST CODER 12B (Q4_K_M) requires 7.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Gemma 4 RUST CODER 12B?
Q4_K_M · 7.9 GB55 devices with unified memory can run Gemma 4 RUST CODER 12B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 4 RUST CODER 12B need?
Gemma 4 RUST CODER 12B requires 7.9 GB of VRAM at Q4_K_M, or 26.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 12B × 4.8 bits ÷ 8 = 7.2 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M7.9 GB- Can NVIDIA GeForce RTX 4090 run Gemma 4 RUST CODER 12B?
Yes, at Q8_0 (13.2 GB) or lower. Higher quantizations like BF16 (26.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Gemma 4 RUST CODER 12B?
For Gemma 4 RUST CODER 12B, Q4_K_M (7.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (9.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 3.6 GB.
VRAM requirement by quantization
IQ2_XXS3.6 GBQ2_K5.6 GBIQ4_XS7.1 GBQ4_K_M ★7.9 GBQ6_K10.9 GBBF1626.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 4 RUST CODER 12B on a Mac?
Gemma 4 RUST CODER 12B requires at least 3.6 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 Gemma 4 RUST CODER 12B locally?
Yes — Gemma 4 RUST CODER 12B can run locally on consumer hardware. At Q4_K_M quantization it needs 7.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 RUST CODER 12B?
At Q4_K_M, Gemma 4 RUST CODER 12B can reach ~556 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~83 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 ÷ 7.9 × 0.65 = ~657 tok/s
Estimated speed at Q4_K_M (7.9 GB)
~657 tok/s~83 tok/s~657 tok/s~556 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 4 RUST CODER 12B?
At Q4_K_M, the download is about 7.20 GB. The full-precision BF16 version is 24.00 GB. The smallest option (IQ2_XXS) is 3.30 GB.
- Which GPUs can run Gemma 4 RUST CODER 12B?
50 consumer GPUs can run Gemma 4 RUST CODER 12B at Q4_K_M (7.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 4 RUST CODER 12B?
59 devices with unified memory can run Gemma 4 RUST CODER 12B at Q4_K_M (7.9 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.