Gemma SEA LION v3 9B IT — Hardware Requirements & GPU Compatibility
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Gemma SEA LION v3 9B IT is a 9.2B-parameter open language model from aisingapore in the Gemma family. It supports a context window of up to 8,192 tokens. At Q4_K_M it needs about 6.46 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 8K)Best on 24 GB (Q4_K_M · ~101 tok/s · 8K)Macs from 32 GB (~13 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma SEA LION v3 9B IT
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve aisingapore/Gemma-SEA-LION-v3-9B-IT --max-model-len 8192vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (4 GB headroom) · Q4_K_M
- Generation speed
- ~36 tok/s
- generation speed
- Cost per 1M output tokens
- $0.53
- per 1M output tokens
How Much VRAM Does Gemma SEA LION v3 9B IT Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.8 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 8K context · ~135 tok/s | |
| 5.4 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 8K context · ~121 tok/s | |
| 6.5 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~101 tok/s | |
| 7.5 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 8K context · ~87 tok/s | |
| 8.5 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 8K context · ~77 tok/s | |
| 10.2 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 8K context · ~65 tok/s | |
| 19.4 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 8K context · ~34 tok/s |
Selected Q4_K_M
4-bit medium quantization7.7GBat 8K context
- Weights
- 5.5 GB
- KV cache
- 1.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.3 GB free · ~101 tok/s
≥50 tok/s needs ≥496 GB/s · RTX 5090 ~180, RTX 3090 Ti ~101
Which Hardware Can Run Gemma SEA LION v3 9B IT?
Q4_K_M · 6.5 GBGemma SEA LION v3 9B IT (Q4_K_M) requires 6.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 9+ GB is recommended. Using the full 8K context window can add up to 1.2 GB, bringing total usage to 7.7 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 159 devices with unified memory can run Gemma SEA LION v3 9B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.3 GB free · up to 8K ctx
16 GPUs · 1 device family
RTX 3080~77Simulate output on RTX 3080RTX 2080 Ti~62Simulate output on RTX 2080 TiRTX 3070 Ti~61Simulate output on RTX 3070 TiOrin Nano~10Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.3 GB free · up to 8K ctx
1 GPU
RX 7900 XT~74Simulate output on RX 7900 XT32–40 GB
fits · 24.3 GB free · up to 8K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~180Simulate output on RTX 5090M5 Max36 GB~50Simulate output on Mac Studio M5 Max (36 GB)M532 GB~17Simulate output on MacBook Air 13" M5 (32 GB)Orin~21Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~14Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Gemma SEA LION v3 9B IT
Gemma family · 16 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Switch 4.1 8BIBM | — | 20.7 GB | ~36 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| ZDTaichu5.0 9BTaichuAI | — | 9.2 GB | ~101 | same size |
| Ornith 1.5 9Bornith-ai | — | 6.6 GB | ~103 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| ZAYA1 VL 8BZyphra | — | 7.5 GB | ~346 | same size |
Specifications
- Publisher
- aisingapore
- Family
- Gemma
- Parameters
- 9.2B
- Architecture
- Gemma2ForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 256,000
- Release Date
- 2024-10-30
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Gemma SEA LION v3 9B IT need?
Gemma SEA LION v3 9B IT requires 6.5 GB of VRAM at Q4_K_M, or 19.4 GB at BF16. Full 8K context adds up to 1.2 GB (7.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.2B × 4.8 bits ÷ 8 = 5.5 GB
KV Cache + Overhead ≈ 1 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.
KV Cache + Overhead ≈ 2.2 GB (at full 8K context)
VRAM usage by quantization
What's the best quantization for Gemma SEA LION v3 9B IT?
For Gemma SEA LION v3 9B IT, Q4_K_M (6.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (7.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma SEA LION v3 9B IT on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run Gemma SEA LION v3 9B IT is iPhone 17 (8 GB) at Q2_K; 140 of the 156 Macs we list can run it at some quantization. For Q4_K_M (6.5 GB) you need a Mac with more unified memory.
Can I run Gemma SEA LION v3 9B IT locally?
Yes — Gemma SEA LION v3 9B IT can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma SEA LION v3 9B IT?
At Q4_K_M, Gemma SEA LION v3 9B IT can reach ~743 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~101 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 ÷ 6.46 × 0.65 = ~805 tok/s
Estimated speed at Q4_K_M (6.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma SEA LION v3 9B IT?
At Q4_K_M, the download is about 5.55 GB. The full-precision BF16 version is 18.48 GB. The smallest option (Q2_K) is 3.93 GB.
Which GPUs can run Gemma SEA LION v3 9B IT?
52 consumer GPUs can run Gemma SEA LION v3 9B IT 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 SEA LION v3 9B IT?
159 devices with unified memory can run Gemma SEA LION v3 9B IT at Q4_K_M (6.5 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.