Inferact·MiniMax·LlamaForCausalLMEagle3

MiniMax M3 EAGLE3 GQA — Hardware Requirements & GPU Compatibility

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MiniMax M3 EAGLE3 GQA is a 3.1B-parameter open language model from Inferact in the MiniMax family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 2.15 GB of VRAM — see which GPUs and Macs can run it below.

1.2K downloads 2 likes1049K context

Specifications

Publisher
Inferact
Family
MiniMax
Parameters
3.1B
Architecture
LlamaForCausalLMEagle3
Context Length
1,048,576 tokens
Vocabulary Size
200,064
Release Date
2026-07-15
License
MIT

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How Much VRAM Does MiniMax M3 EAGLE3 GQA Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.401.6 GB
Q3_K_Mest.3.901.8 GB
Q4_K_Mest.4.802.1 GB
Q5_K_Mest.5.702.5 GB
Q6_Kest.6.602.8 GB
Q8_0est.8.003.4 GB
BF16est.16.006.5 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 MiniMax M3 EAGLE3 GQA?

Q4_K_M · 2.1 GB

MiniMax M3 EAGLE3 GQA (Q4_K_M) requires 2.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 1049K context window can add up to 1.6 GB, bringing total usage to 3.8 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

Plenty of headroom
NVIDIA GeForce RTX 5090~542 tok/sNVIDIA GeForce RTX 3090 Ti~305 tok/sNVIDIA GeForce RTX 4090~305 tok/sNVIDIA GeForce RTX 5080~290 tok/sNVIDIA GeForce RTX 3090~283 tok/sNVIDIA GeForce RTX 3080 Ti~276 tok/sNVIDIA GeForce RTX 5070 Ti~271 tok/sNVIDIA GeForce RTX 5090 Laptop GPU~271 tok/sAMD Radeon RX 7900 XTX~246 tok/sNVIDIA GeForce RTX 3080~230 tok/sNVIDIA GeForce RTX 4080 SUPER~223 tok/sNVIDIA GeForce RTX 4080~217 tok/sAMD Radeon RX 7900 XT~205 tok/sNVIDIA GeForce RTX 4070 Ti SUPER~203 tok/sNVIDIA GeForce RTX 5070~203 tok/sNVIDIA TITAN RTX~203 tok/sNVIDIA GeForce RTX 2080 Ti~186 tok/sNVIDIA GeForce RTX 3070 Ti~184 tok/sNVIDIA GeForce RTX 4090 Laptop GPU~174 tok/sAMD Radeon RX 9070~164 tok/sAMD Radeon RX 9070 XT~164 tok/sAMD Radeon RX 7800 XT~160 tok/sNVIDIA GeForce RTX 4070~152 tok/sNVIDIA GeForce RTX 4070 SUPER~152 tok/sNVIDIA GeForce RTX 4070 Ti~152 tok/sAMD Radeon RX 7900 GRE~147 tok/sNVIDIA GeForce GTX 1080 Ti~146 tok/sNVIDIA GeForce RTX 3060 Ti~135 tok/sNVIDIA GeForce RTX 3070~135 tok/sNVIDIA GeForce RTX 5060~135 tok/sNVIDIA GeForce RTX 5060 Ti 16GB~135 tok/sNVIDIA GeForce RTX 5060 Ti 8GB~135 tok/sAMD Radeon RX 6800~131 tok/sAMD Radeon RX 6800 XT~131 tok/sAMD Radeon RX 6900 XT~131 tok/sIntel Arc A770 16GB~130 tok/sIntel Arc A750~119 tok/sAMD Radeon RX 7700 XT~111 tok/sNVIDIA GeForce RTX 3060 12GB~109 tok/sIntel Arc B580~106 tok/sAMD Radeon RX 6700 XT~98 tok/sIntel Arc B570~88 tok/sNVIDIA GeForce RTX 4060 Ti 16GB~87 tok/sNVIDIA GeForce RTX 4060 Ti 8GB~87 tok/sNVIDIA GeForce RTX 4060~82 tok/sAMD Radeon RX 9060 XT 16GB~82 tok/sAMD Radeon RX 7600~74 tok/sAMD Radeon RX 7600 XT~74 tok/sNVIDIA GeForce RTX 3060 8GB~73 tok/sNVIDIA GeForce RTX 3050 8GB~68 tok/s

Which Devices Can Run MiniMax M3 EAGLE3 GQA?

Q4_K_M · 2.1 GB

59 devices with unified memory can run MiniMax M3 EAGLE3 GQA, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Runs great

Plenty of headroom
NVIDIA DGX H100~8102 tok/sNVIDIA DGX A100 640GB~4932 tok/sMac Studio (M3 Ultra, 256GB)~267 tok/sMac Studio (M3 Ultra, 512GB)~267 tok/sMac Studio (M3 Ultra, 96GB)~267 tok/sMac Pro M2 Ultra (192 GB)~261 tok/sMac Studio M2 Ultra (192 GB)~261 tok/sMacBook Pro 16" M5 Max (128 GB)~200 tok/sMac Studio M4 Max (128 GB)~178 tok/sMac Studio M4 Max (64 GB)~178 tok/sMacBook Pro 16" M4 Max (48 GB)~178 tok/sMacBook Pro 16" M4 Max (64 GB)~178 tok/sMac Studio M4 Max (36 GB)~133 tok/sMacBook Pro 14" M4 Max (36 GB)~133 tok/sMacBook Pro 16" M3 Max (48 GB)~133 tok/sMacBook Pro 14-inch (M5 Pro)~100 tok/sMac Mini M4 Pro (24 GB)~89 tok/sMac Mini M4 Pro (48 GB)~89 tok/sMacBook Pro 14" M4 Pro (24 GB)~89 tok/sMacBook Pro 16" M4 Pro (24 GB)~89 tok/sASUS Ascent GX10~83 tok/sNVIDIA DGX Spark~83 tok/sNVIDIA Jetson AGX Thor Developer Kit~83 tok/sAsus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB)~77 tok/sBeelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB)~77 tok/sFramework Desktop (Ryzen AI Max+ 395, 128 GB)~77 tok/sGMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB)~77 tok/sHP Z2 Mini G1a (Ryzen AI Max+ PRO 395, 128 GB)~77 tok/sHP ZBook Ultra G1a 14 (Ryzen AI Max+ PRO 395, 128 GB)~77 tok/sMinisforum MS-S1 MAX (Ryzen AI Max+ 395, 128 GB)~77 tok/sSnapdragon X2 Elite Extreme Copilot+ PC~69 tok/sNVIDIA Jetson AGX Orin 32GB~62 tok/sNVIDIA Jetson AGX Orin 64GB~62 tok/sMacBook Pro 14-inch (M5)~50 tok/siPad Pro M5 13" (16 GB)~50 tok/sSnapdragon X Elite Copilot+ PC~41 tok/sMac Mini M4 (16 GB)~39 tok/sMac Mini M4 (32 GB)~39 tok/sMacBook Air 13" M4 (16 GB)~39 tok/sMacBook Air 13" M4 (24 GB)~39 tok/sMacBook Air 15" M4 (16 GB)~39 tok/sMacBook Air 15" M4 (24 GB)~39 tok/sMacBook Pro 14" M4 (16 GB)~39 tok/siPad Pro M4 13" (16 GB)~39 tok/sMacBook Air 13" M3 (16 GB)~33 tok/sMacBook Air 13" M3 (24 GB)~33 tok/sMacBook Air 13" M3 (8 GB)~33 tok/sIntel Core Ultra 9 288V (Lunar Lake) Laptop~32 tok/sNVIDIA Jetson Orin NX 16GB~31 tok/sNVIDIA Jetson Orin Nano 8GB (Super)~31 tok/sAMD Ryzen AI 9 HX 370 (Strix Point) Laptop~31 tok/sApple iPhone 17 Pro~25 tok/siPhone 17 Pro Max~25 tok/siPhone 17~22 tok/siPhone Air~22 tok/siPhone 15 ProiPhone 15 Pro MaxiPhone 16 ProiPhone 16 Pro Max

Related Models

Frequently Asked Questions

How much VRAM does MiniMax M3 EAGLE3 GQA need?

MiniMax M3 EAGLE3 GQA requires 2.1 GB of VRAM at Q4_K_M, or 6.5 GB at BF16. Full 1049K context adds up to 1.6 GB (3.8 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 3.1B × 4.8 bits ÷ 8 = 1.8 GB

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

KV Cache + Overhead 2 GB (at full 1049K context)

VRAM usage by quantization

2.1 GB
3.8 GB

Learn more about VRAM estimation →

What's the best quantization for MiniMax M3 EAGLE3 GQA?

For MiniMax M3 EAGLE3 GQA, Q4_K_M (2.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.6 GB.

VRAM requirement by quantization

Q2_K
1.6 GB
Q4_K_M
2.1 GB
Q5_K_M
2.5 GB
Q6_K
2.8 GB
Q8_0
3.4 GB
BF16
6.5 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run MiniMax M3 EAGLE3 GQA on a Mac?

MiniMax M3 EAGLE3 GQA requires at least 1.6 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 MiniMax M3 EAGLE3 GQA locally?

Yes — MiniMax M3 EAGLE3 GQA can run locally on consumer hardware. At Q4_K_M quantization it needs 2.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is MiniMax M3 EAGLE3 GQA?

At Q4_K_M, MiniMax M3 EAGLE3 GQA can reach ~2047 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~305 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 ÷ 2.1 × 0.65 = ~2419 tok/s

Estimated speed at Q4_K_M (2.1 GB)

~2419 tok/s
~305 tok/s
~2419 tok/s
~2047 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 MiniMax M3 EAGLE3 GQA?

At Q4_K_M, the download is about 1.84 GB. The full-precision BF16 version is 6.15 GB. The smallest option (Q2_K) is 1.31 GB.

Which GPUs can run MiniMax M3 EAGLE3 GQA?

50 consumer GPUs can run MiniMax M3 EAGLE3 GQA at Q4_K_M (2.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.

Which devices can run MiniMax M3 EAGLE3 GQA?

59 devices with unified memory can run MiniMax M3 EAGLE3 GQA at Q4_K_M (2.1 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.