Gemma 4 31B IT Control Vectors — Hardware Requirements & GPU Compatibility
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Gemma 4 31B IT Control Vectors is a 31B-parameter open language model from gghfez in the Gemma 4 family. At Q4_K_M it needs about 20.46 GB of VRAM — see which GPUs and Macs can run it below.
Runs well on a 24 GB GPU
Runs on 24 GB (Q4_K_M · ~32 tok/s · 8K)Macs from 24 GB (Q4_K_M · ~11 tok/s)
CC BY-NC 4.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 4 31B IT Control Vectors
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve gghfez/gemma-4-31b-it-control-vectors --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 A6000 (48 GB) (23 GB headroom) · Q4_K_M
- Generation speed
- ~24 tok/s
- generation speed
- Cost per 1M output tokens
- $3.76
- per 1M output tokens
How Much VRAM Does Gemma 4 31B IT Control Vectors Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 14.5 GB | 22.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~45 tok/s | |
| 16.6 GB | 24.7 GB | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~39 tok/s | |
| 20.5 GB | 28.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~32 tok/s | |
| 24.3 GB | 32.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 28.1 GB | |
| 28.1 GB | 36.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 31.9 GB | |
| 34.1 GB | 42.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 37.9 GB | |
| 68.2 GB | 76.2 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 72 GB |
Selected Q4_K_M
4-bit medium quantization24.2GBat 16K context
- Weights
- 19.6 GB
- KV cache
- 4.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · fits up to 8K context · ~32 tok/s
≥50 tok/s needs ≥1.57 TB/s · RTX 5090 ~57
Which Hardware Can Run Gemma 4 31B IT Control Vectors?
Q4_K_M · 20.5 GBGemma 4 31B IT Control Vectors (Q4_K_M) requires 20.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Gemma 4 31B IT Control Vectors, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
Q4_K_M · best fit Q2_K (18.2 GB)
1 GPU
RX 7900 XT~33Simulate output on RX 7900 XT32–40 GB
fits · 7.8 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~57Simulate output on RTX 5090M5 Max36 GB~16Simulate output on Mac Studio M5 Max (36 GB)M532 GB~5Simulate output on MacBook Air 13" M5 (32 GB)Orin~7Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~4Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Gemma 4 31B IT Control Vectors
Gemma 4 family · 35 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Hy Memory 30B A3BTencent | — | 19.1 GB | ~184 | same size |
| Llm Jp 4.1 32B A3b Thinkingllm-jp | — | 20.1 GB | ~188 | same size |
| Llm Jp 4.1 33B Thinkingllm-jp | — | 22.5 GB | ~35 | same size |
| Agnes 3.0 FlashAgnes-AI | — | 22.1 GB | ~35 | same size |
| Muse Glimmer 30BMeta | — | 19.6 GB | ~36 | same size |
| NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA | — | 19.3 GB | ~197 | same size |
Specifications
- Publisher
- gghfez
- Family
- Gemma 4
- Parameters
- 31B
- Release Date
- 2026-04-11
- License
- CC BY-NC 4.0
Frequently Asked Questions
How much VRAM does Gemma 4 31B IT Control Vectors need?
Gemma 4 31B IT Control Vectors requires 20.5 GB of VRAM at Q4_K_M, or 68.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 31B × 4.8 bits ÷ 8 = 18.6 GB
KV Cache + Overhead ≈ 1.9 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 NVIDIA GeForce RTX 4090 run Gemma 4 31B IT Control Vectors?
Yes, at Q4_K_M (20.5 GB) or lower. Higher quantizations like Q5_K_M (24.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Gemma 4 31B IT Control Vectors?
For Gemma 4 31B IT Control Vectors, Q4_K_M (20.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (24.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 4 31B IT Control Vectors on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Gemma 4 31B IT Control Vectors. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 20.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 4 31B IT Control Vectors locally?
Yes — Gemma 4 31B IT Control Vectors can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 4 31B IT Control Vectors?
At Q4_K_M, Gemma 4 31B IT Control Vectors can reach ~235 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 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 ÷ 20.5 × 0.65 = ~254 tok/s
Estimated speed at Q4_K_M (20.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma 4 31B IT Control Vectors?
At Q4_K_M, the download is about 18.60 GB. The full-precision BF16 version is 62.00 GB. The smallest option (Q2_K) is 13.18 GB.
Which GPUs can run Gemma 4 31B IT Control Vectors?
7 consumer GPUs can run Gemma 4 31B IT Control Vectors at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Gemma 4 31B IT Control Vectors?
122 devices with unified memory can run Gemma 4 31B IT Control Vectors at Q4_K_M (20.5 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.