IBM·Granite·GraniteMoeHybridForCausalLM

Granite 4.0 1B Base — Hardware Requirements & GPU Compatibility

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Granite 4.0 1B Base is a 1.6B-parameter open language model from IBM in the Granite family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 1.45 GB of VRAM — see which GPUs and Macs can run it below.

40.6K downloads 32 likes131K context

Specifications

Publisher
IBM
Family
Granite
Parameters
1.6B
Architecture
GraniteMoeHybridForCausalLM
Context Length
131,072 tokens
Vocabulary Size
100,352
Release Date
2025-10-07
License
Apache 2.0

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How Much VRAM Does Granite 4.0 1B Base Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.401.2 GB
Q3_K_Mest.3.901.3 GB
Q4_K_Mest.4.801.4 GB
Q5_K_Mest.5.701.6 GB
Q6_Kest.6.601.8 GB
Q8_0est.8.002.1 GB
BF16est.16.003.7 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 Granite 4.0 1B Base?

Q4_K_M · 1.4 GB

Granite 4.0 1B Base (Q4_K_M) requires 1.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 131K context window can add up to 10.6 GB, bringing total usage to 12.0 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

— Plenty of headroom
NVIDIA GeForce RTX 5090~803 tok/sNVIDIA GeForce RTX 3090 Ti~452 tok/sNVIDIA GeForce RTX 4090~452 tok/sNVIDIA GeForce RTX 5080~430 tok/sNVIDIA GeForce RTX 3090~420 tok/sNVIDIA GeForce RTX 3080 Ti~409 tok/sNVIDIA GeForce RTX 5070 Ti~402 tok/sNVIDIA GeForce RTX 5090 Laptop GPU~402 tok/sAMD Radeon RX 7900 XTX~397 tok/sNVIDIA GeForce RTX 3080~341 tok/sAMD Radeon RX 7900 XT~331 tok/sNVIDIA GeForce RTX 4080 SUPER~330 tok/sNVIDIA GeForce RTX 4080~321 tok/sNVIDIA GeForce RTX 4070 Ti SUPER~301 tok/sNVIDIA GeForce RTX 5070~301 tok/sNVIDIA TITAN RTX~301 tok/sNVIDIA GeForce RTX 2080 Ti~276 tok/sNVIDIA GeForce RTX 3070 Ti~273 tok/sAMD Radeon RX 9070~265 tok/sAMD Radeon RX 9070 XT~265 tok/sAMD Radeon RX 7800 XT~258 tok/sNVIDIA GeForce RTX 4090 Laptop GPU~258 tok/sAMD Radeon RX 7900 GRE~238 tok/sNVIDIA GeForce RTX 4070~226 tok/sNVIDIA GeForce RTX 4070 SUPER~226 tok/sNVIDIA GeForce RTX 4070 Ti~226 tok/sNVIDIA GeForce GTX 1080 Ti~217 tok/sAMD Radeon RX 6800~212 tok/sAMD Radeon RX 6800 XT~212 tok/sAMD Radeon RX 6900 XT~212 tok/sNVIDIA GeForce RTX 3060 Ti~201 tok/sNVIDIA GeForce RTX 3070~201 tok/sNVIDIA GeForce RTX 5060~201 tok/sNVIDIA GeForce RTX 5060 Ti 16GB~201 tok/sNVIDIA GeForce RTX 5060 Ti 8GB~201 tok/sIntel Arc A770 16GB~193 tok/sAMD Radeon RX 7700 XT~179 tok/sAMD Radeon RX 9070 GRE~179 tok/sIntel Arc A750~177 tok/sNVIDIA GeForce RTX 3060 12GB~161 tok/sAMD Radeon RX 6700 XT~159 tok/sIntel Arc B580~157 tok/sAMD Radeon RX 9060 XT 16GB~132 tok/sIntel Arc B570~131 tok/sNVIDIA GeForce RTX 4060 Ti 16GB~129 tok/sNVIDIA GeForce RTX 4060 Ti 8GB~129 tok/sNVIDIA GeForce RTX 4060~122 tok/sAMD Radeon RX 7600~119 tok/sAMD Radeon RX 7600 XT~119 tok/sAMD Radeon RX 9050~119 tok/sNVIDIA GeForce RTX 3060 8GB~108 tok/sNVIDIA GeForce RTX 3050 8GB~100 tok/s

Which Devices Can Run Granite 4.0 1B Base?

Q4_K_M · 1.4 GB

59 devices with unified memory can run Granite 4.0 1B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Runs great

— Plenty of headroom
NVIDIA DGX H100~12014 tok/sNVIDIA DGX A100 640GB~7312 tok/sMac Studio (M3 Ultra, 256GB)~395 tok/sMac Studio (M3 Ultra, 512GB)~395 tok/sMac Studio (M3 Ultra, 96GB)~395 tok/sMac Pro M2 Ultra (192 GB)~386 tok/sMac Studio M2 Ultra (192 GB)~386 tok/sMacBook Pro 16" M5 Max (128 GB)~296 tok/sMac Studio M4 Max (128 GB)~264 tok/sMac Studio M4 Max (64 GB)~264 tok/sMacBook Pro 16" M4 Max (48 GB)~264 tok/sMacBook Pro 16" M4 Max (64 GB)~264 tok/sMac Studio M4 Max (36 GB)~198 tok/sMacBook Pro 14" M4 Max (36 GB)~198 tok/sMacBook Pro 16" M3 Max (48 GB)~198 tok/sMacBook Pro 14-inch (M5 Pro)~148 tok/sMac Mini M4 Pro (24 GB)~132 tok/sMac Mini M4 Pro (48 GB)~132 tok/sMacBook Pro 14" M4 Pro (24 GB)~132 tok/sMacBook Pro 16" M4 Pro (24 GB)~132 tok/sASUS Ascent GX10~122 tok/sNVIDIA DGX Spark~122 tok/sNVIDIA Jetson AGX Thor Developer Kit~122 tok/sAsus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB)~115 tok/sBeelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB)~115 tok/sFramework Desktop (Ryzen AI Max+ 395, 128 GB)~115 tok/sGMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB)~115 tok/sHP Z2 Mini G1a (Ryzen AI Max+ PRO 395, 128 GB)~115 tok/sHP ZBook Ultra G1a 14 (Ryzen AI Max+ PRO 395, 128 GB)~115 tok/sMinisforum MS-S1 MAX (Ryzen AI Max+ 395, 128 GB)~115 tok/sSnapdragon X2 Elite Extreme Copilot+ PC~102 tok/sNVIDIA Jetson AGX Orin 32GB~92 tok/sNVIDIA Jetson AGX Orin 64GB~92 tok/sMacBook Pro 14-inch (M5)~74 tok/siPad Pro M5 13" (16 GB)~74 tok/sSnapdragon X Elite Copilot+ PC~61 tok/sMac Mini M4 (16 GB)~58 tok/sMac Mini M4 (32 GB)~58 tok/sMacBook Air 13" M4 (16 GB)~58 tok/sMacBook Air 13" M4 (24 GB)~58 tok/sMacBook Air 15" M4 (16 GB)~58 tok/sMacBook Air 15" M4 (24 GB)~58 tok/sMacBook Pro 14" M4 (16 GB)~58 tok/siPad Pro M4 13" (16 GB)~58 tok/sAMD Ryzen AI 9 HX 370 (Strix Point) Laptop~50 tok/sMacBook Air 13" M3 (16 GB)~49 tok/sMacBook Air 13" M3 (24 GB)~49 tok/sMacBook Air 13" M3 (8 GB)~49 tok/sIntel Core Ultra 9 288V (Lunar Lake) Laptop~47 tok/sNVIDIA Jetson Orin NX 16GB~46 tok/sNVIDIA Jetson Orin Nano 8GB (Super)~46 tok/sApple iPhone 17 Pro~37 tok/siPhone 17 Pro Max~37 tok/siPhone 17~33 tok/siPhone Air~33 tok/siPhone 15 ProiPhone 15 Pro MaxiPhone 16 ProiPhone 16 Pro Max

Related Models

Frequently Asked Questions

How much VRAM does Granite 4.0 1B Base need?

Granite 4.0 1B Base requires 1.4 GB of VRAM at Q4_K_M, or 3.7 GB at BF16. Full 131K context adds up to 10.6 GB (12.0 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 1.6B × 4.8 bits ÷ 8 = 1 GB

KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead ≈ 11 GB (at full 131K context)

VRAM usage by quantization

1.4 GB
12.0 GB

Learn more about VRAM estimation →

What's the best quantization for Granite 4.0 1B Base?

For Granite 4.0 1B Base, Q4_K_M (1.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.2 GB.

VRAM requirement by quantization

Q2_K
1.2 GB
Q4_K_M ★
1.4 GB
Q5_K_M
1.6 GB
Q6_K
1.8 GB
Q8_0
2.1 GB
BF16
3.7 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Granite 4.0 1B Base on a Mac?

Granite 4.0 1B Base requires at least 1.2 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 Granite 4.0 1B Base locally?

Yes — Granite 4.0 1B Base can run locally on consumer hardware. At Q4_K_M quantization it needs 1.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Granite 4.0 1B Base?

At Q4_K_M, Granite 4.0 1B Base can reach ~3310 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~452 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 ÷ 1.4 × 0.65 = ~3586 tok/s

Estimated speed at Q4_K_M (1.4 GB)

~3586 tok/s
~452 tok/s
~3586 tok/s
~3310 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 Granite 4.0 1B Base?

At Q4_K_M, the download is about 0.98 GB. The full-precision BF16 version is 3.26 GB. The smallest option (Q2_K) is 0.69 GB.

Which GPUs can run Granite 4.0 1B Base?

52 consumer GPUs can run Granite 4.0 1B Base at Q4_K_M (1.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.

Which devices can run Granite 4.0 1B Base?

59 devices with unified memory can run Granite 4.0 1B Base at Q4_K_M (1.4 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.