Gemma 1.1 7B IT — Hardware Requirements & GPU Compatibility
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Gemma 1.1 7B IT is a 8.5B-parameter open language model from Google in the Gemma family. At BF16 it needs about 18.78 GB of VRAM — see which GPUs and Macs can run it below.
Decent on a 24 GB GPU
Runs on 24 GB (BF16 · ~35 tok/s · 16K)Macs from 24 GB (BF16 · ~11 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 1.1 7B IT
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve google/gemma-1.1-7b-it --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 5090 (32 GB) (10 GB headroom) · BF16
- Generation speed
- ~62 tok/s
- generation speed
- Cost per 1M output tokens
- $2.23
- per 1M output tokens
How Much VRAM Does Gemma 1.1 7B IT Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 18.8 GB | 24.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 16K context · ~35 tok/s |
Selected BF16
Brain floating point 1621.4GBat 16K context
- Weights
- 18.1 GB
- KV cache
- 3.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 2.6 GB free · ~35 tok/s
≥50 tok/s needs ≥1.44 TB/s · RTX 5090 ~62
Which Hardware Can Run Gemma 1.1 7B IT?
BF16 · 18.8 GBGemma 1.1 7B IT (BF16) requires 18.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 25+ GB is recommended. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Gemma 1.1 7B IT, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).
20 GB
tight · up to 8K ctx
1 GPU
RX 7900 XT~26Simulate output on RX 7900 XT32–40 GB
fits · 10.6 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~62Simulate output on RTX 5090M5 Max36 GB~17Simulate output on Mac Studio M5 Max (36 GB)M532 GB~6Simulate output on MacBook Air 13" M5 (32 GB)Orin~7Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~5Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
How good is Gemma 1.1 7B IT?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
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Alternatives to Gemma 1.1 7B IT
Gemma family · 17 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
Specifications
- Publisher
- Family
- Gemma
- Parameters
- 8.5B
- Release Date
- 2024-03-26
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Gemma 1.1 7B IT need?
Gemma 1.1 7B IT requires 18.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.5B × 16 bits ÷ 8 = 17.1 GB
KV Cache + Overhead ≈ 1.7 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 I run Gemma 1.1 7B IT on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Gemma 1.1 7B IT. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 18.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 1.1 7B IT locally?
Yes — Gemma 1.1 7B IT can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 1.1 7B IT?
At BF16, Gemma 1.1 7B IT can reach ~256 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~35 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 ÷ 18.8 × 0.65 = ~277 tok/s
Estimated speed at BF16 (18.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma 1.1 7B IT?
At BF16, the download is about 17.08 GB.
Which GPUs can run Gemma 1.1 7B IT?
8 consumer GPUs can run Gemma 1.1 7B IT at BF16. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Gemma 1.1 7B IT?
122 devices with unified memory can run Gemma 1.1 7B IT at BF16 (18.8 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.