Gemma 7B — Hardware Requirements & GPU Compatibility
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Google Gemma 7B is a 7-billion parameter base (pretrained) model from the original Gemma generation, Google's first openly available family of language models. It represents Google's initial entry into the open-weight LLM space. While superseded by Gemma 2 and Gemma 3 in terms of benchmark performance, the original Gemma 7B remains a solid foundation model and a useful reference point in the evolution of Google's open models. Released under the Gemma license.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 8K)Best on 24 GB (Q4_K_M · ~116 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~25 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 7B
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
ollama run hf.co/MaziyarPanahi/gemma-7b-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 15.8 GB free · 16K context
Download: gemma-7b.Q4_K_M.gguf5.33 GBMaziyarPanahi
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (3 GB headroom) · Q4_K_M
- Generation speed
- ~105 tok/s
- generation speed
- Cost per 1M output tokens
- $0.32
- per 1M output tokens
How Much VRAM Does Gemma 7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.0 GB | 9.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~164 tok/s | |
| 4.1 GB | 9.7 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~159 tok/s | |
| 4.6 GB | 10.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~143 tok/s | |
| 4.7 GB | 10.3 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~139 tok/s | |
| 5.6 GB | 11.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~116 tok/s | |
| 6.7 GB | 12.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~98 tok/s | |
| 7.8 GB | 13.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~85 tok/s | |
| 9.4 GB | 15 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~70 tok/s |
Selected Q4_K_M
4-bit medium quantization8.2GBat 16K context
- Weights
- 5.0 GB
- KV cache
- 3.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 15.8 GB free · ~116 tok/s
≥50 tok/s needs ≥433 GB/s · RTX 5090 ~207, RTX 3090 Ti ~116
Which Hardware Can Run Gemma 7B?
Q4_K_M · 5.6 GBGemma 7B (Q4_K_M) requires 5.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Gemma 7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
12 GB
12 GB tier →fits · 3.8 GB free · up to 32K ctx
10 GPUs · 1 device family
RTX 3080 Ti~105Simulate output on RTX 3080 TiRTX 5070~78Simulate output on RTX 5070RTX 4070~58Simulate output on RTX 4070iPhone~10Simulate output on Apple iPhone 17 Pro20 GB
fits · 11.8 GB free · up to 32K ctx
1 GPU
RX 7900 XT~85Simulate output on RX 7900 XT32–40 GB
fits · 23.8 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~207Simulate output on RTX 5090M5 Max36 GB~57Simulate output on Mac Studio M5 Max (36 GB)M532 GB~19Simulate output on MacBook Air 13" M5 (32 GB)Orin~24Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~16Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Gemma 7B?
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
The 6 boards that weigh most in the llmrun Score, of 11 with a result for this model; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent33.7%
#114/146 open
- MMLU
Epoch AI · Feb 2024Lab-reported
Lab-reported66.1%
#37/85 open
- BIG-Bench Hard
Epoch AI · Feb 2024Lab-reported
Lab-reported55.1%
#15/38 open
- GSM8K
Epoch AI · Feb 2024Lab-reported
Lab-reported46.4%
#33/62 open
- HellaSwag
Epoch AI · Feb 2024Lab-reported
Lab-reported82.2%
#13/47 open
- ARC Challenge
Epoch AI · Feb 2024Lab-reported
Lab-reported78.3%
#14/56 open
Alternatives to Gemma 7B
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 |
Fine-tunes & variants(3)
Specifications
- Publisher
- Family
- Gemma
- Parameters
- 8.5B
- Release Date
- 2024-02-08
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Gemma 7B need?
Gemma 7B requires 5.6 GB of VRAM at Q4_K_M, or 18.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.5B × 4.8 bits ÷ 8 = 5.1 GB
KV Cache + Overhead ≈ 0.5 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
What's the best quantization for Gemma 7B?
For Gemma 7B, Q4_K_M (5.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 7B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Gemma 7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 7B locally?
Yes — Gemma 7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 7B?
At Q4_K_M, Gemma 7B can reach ~853 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~116 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 ÷ 5.63 × 0.65 = ~924 tok/s
Estimated speed at Q4_K_M (5.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma 7B?
At Q4_K_M, the download is about 5.12 GB. The full-precision BF16 version is 17.08 GB. The smallest option (IQ2_XXS) is 2.35 GB.
Which GPUs can run Gemma 7B?
52 consumer GPUs can run Gemma 7B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Gemma 7B?
151 devices with unified memory can run Gemma 7B at Q4_K_M (5.6 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.