Unbabel·Gemma2ForCausalLM

Tower Plus 9B — Hardware Requirements & GPU Compatibility

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Tower Plus 9B is a 9.2B-parameter open language model from Unbabel. 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.

7.2K downloads 36 likes8K context
Based on Gemma 2 9B

Specifications

Publisher
Unbabel
Parameters
9.2B
Architecture
Gemma2ForCausalLM
Context Length
8,192 tokens
Vocabulary Size
256,000
Release Date
2025-06-09
License
CC BY-NC-SA 4.0

Get Started

How Much VRAM Does Tower Plus 9B Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.404.8 GB
Q3_K_Mest.3.905.4 GB
Q4_K_Mest.4.806.5 GB
Q5_K_Mest.5.707.5 GB
Q6_Kest.6.608.5 GB
Q8_0est.8.0010.2 GB
BF16est.16.0019.4 GB

est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.

Which GPUs Can Run Tower Plus 9B?

Q4_K_M · 6.5 GB

Tower Plus 9B (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 1.9 GB, bringing total usage to 8.3 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.

Runs great

Plenty of headroom

Which Devices Can Run Tower Plus 9B?

Q4_K_M · 6.5 GB

58 devices with unified memory can run Tower Plus 9B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).

Runs great

Plenty of headroom
NVIDIA DGX H100~2697 tok/sNVIDIA DGX A100 640GB~1641 tok/sMac Studio (M3 Ultra, 256GB)~89 tok/sMac Studio (M3 Ultra, 512GB)~89 tok/sMac Studio (M3 Ultra, 96GB)~89 tok/sMac Pro M2 Ultra (192 GB)~87 tok/sMac Studio M2 Ultra (192 GB)~87 tok/sMacBook Pro 16" M5 Max (128 GB)~67 tok/sMac Studio M4 Max (128 GB)~59 tok/sMac Studio M4 Max (64 GB)~59 tok/sMacBook Pro 16" M4 Max (48 GB)~59 tok/sMacBook Pro 16" M4 Max (64 GB)~59 tok/sMac Studio M4 Max (36 GB)~44 tok/sMacBook Pro 14" M4 Max (36 GB)~44 tok/sMacBook Pro 16" M3 Max (48 GB)~44 tok/sMacBook Pro 14-inch (M5 Pro)~33 tok/sMac Mini M4 Pro (24 GB)~30 tok/sMac Mini M4 Pro (48 GB)~30 tok/sMacBook Pro 14" M4 Pro (24 GB)~30 tok/sMacBook Pro 16" M4 Pro (24 GB)~30 tok/sASUS Ascent GX10~28 tok/sNVIDIA DGX Spark~28 tok/sNVIDIA Jetson AGX Thor Developer Kit~28 tok/sAsus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB)~26 tok/sBeelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB)~26 tok/sFramework Desktop (Ryzen AI Max+ 395, 128 GB)~26 tok/sGMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB)~26 tok/sHP Z2 Mini G1a (Ryzen AI Max+ PRO 395, 128 GB)~26 tok/sHP ZBook Ultra G1a 14 (Ryzen AI Max+ PRO 395, 128 GB)~26 tok/sMinisforum MS-S1 MAX (Ryzen AI Max+ 395, 128 GB)~26 tok/sSnapdragon X2 Elite Extreme Copilot+ PC~23 tok/sNVIDIA Jetson AGX Orin 32GB~21 tok/sNVIDIA Jetson AGX Orin 64GB~21 tok/sMacBook Pro 14-inch (M5)~17 tok/siPad Pro M5 13" (16 GB)~17 tok/sSnapdragon X Elite Copilot+ PC~14 tok/sMac Mini M4 (16 GB)~13 tok/sMac Mini M4 (32 GB)~13 tok/sMacBook Air 13" M4 (16 GB)~13 tok/sMacBook Air 13" M4 (24 GB)~13 tok/sMacBook Air 15" M4 (16 GB)~13 tok/sMacBook Air 15" M4 (24 GB)~13 tok/sMacBook Pro 14" M4 (16 GB)~13 tok/siPad Pro M4 13" (16 GB)~13 tok/sMacBook Air 13" M3 (16 GB)~11 tok/sMacBook Air 13" M3 (24 GB)~11 tok/sIntel Core Ultra 9 288V (Lunar Lake) Laptop~11 tok/sNVIDIA Jetson Orin NX 16GB~10 tok/sAMD Ryzen AI 9 HX 370 (Strix Point) Laptop~10 tok/s

Related Models

Frequently Asked Questions

How much VRAM does Tower Plus 9B need?

Tower Plus 9B requires 6.5 GB of VRAM at Q4_K_M, or 19.4 GB at BF16. Full 8K context adds up to 1.9 GB (8.3 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)

KV Cache + Overhead 2.8 GB (at full 8K context)

VRAM usage by quantization

6.5 GB
8.3 GB

Learn more about VRAM estimation →

What's the best quantization for Tower Plus 9B?

For Tower Plus 9B, 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

Q2_K
4.8 GB
Q4_K_M
6.5 GB
Q5_K_M
7.5 GB
Q6_K
8.5 GB
Q8_0
10.2 GB
BF16
19.4 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Tower Plus 9B on a Mac?

Tower Plus 9B requires at least 4.8 GB at Q2_K, 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 Tower Plus 9B locally?

Yes — Tower Plus 9B can run locally on consumer hardware. At Q4_K_M quantization it needs 6.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Tower Plus 9B?

At Q4_K_M, Tower Plus 9B can reach ~681 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 B2008000 ÷ 6.5 × 0.65 = ~805 tok/s

Estimated speed at Q4_K_M (6.5 GB)

~805 tok/s
~101 tok/s
~805 tok/s
~681 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Tower Plus 9B?

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 Tower Plus 9B?

50 consumer GPUs can run Tower Plus 9B at Q4_K_M (6.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.

Which devices can run Tower Plus 9B?

59 devices with unified memory can run Tower Plus 9B 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.