utter-project·MixtralForCausalLM

EuroMoE 2.6B A0.6B 2512 — Hardware Requirements & GPU Compatibility

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EuroMoE 2.6B A0.6B 2512 is a 2.6B-parameter open language model from utter-project. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 1.92 GB of VRAM — see which GPUs and Macs can run it below.

1.5K downloads 8 likes4K context

Specifications

Publisher
utter-project
Parameters
2.6B
Architecture
MixtralForCausalLM
Context Length
4,096 tokens
Vocabulary Size
128,000
Release Date
2025-06-09
License
Apache 2.0

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How Much VRAM Does EuroMoE 2.6B A0.6B 2512 Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.401.5 GB
Q3_K_Mest.3.901.6 GB
Q4_K_Mest.4.801.9 GB
Q5_K_Mest.5.702.2 GB
Q6_Kest.6.602.5 GB
Q8_0est.8.003.0 GB
BF16est.16.005.6 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 EuroMoE 2.6B A0.6B 2512?

Q4_K_M · 1.9 GB

EuroMoE 2.6B A0.6B 2512 (Q4_K_M) requires 1.9 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 4K context window can add up to 0.1 GB, bringing total usage to 2.0 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

Plenty of headroom
NVIDIA GeForce RTX 5090~607 tok/sNVIDIA GeForce RTX 3090 Ti~341 tok/sNVIDIA GeForce RTX 4090~341 tok/sNVIDIA GeForce RTX 5080~325 tok/sNVIDIA GeForce RTX 3090~317 tok/sNVIDIA GeForce RTX 3080 Ti~309 tok/sNVIDIA GeForce RTX 5070 Ti~303 tok/sNVIDIA GeForce RTX 5090 Laptop GPU~303 tok/sAMD Radeon RX 7900 XTX~275 tok/sNVIDIA GeForce RTX 3080~257 tok/sNVIDIA GeForce RTX 4080 SUPER~249 tok/sNVIDIA GeForce RTX 4080~243 tok/sAMD Radeon RX 7900 XT~229 tok/sNVIDIA GeForce RTX 4070 Ti SUPER~228 tok/sNVIDIA GeForce RTX 5070~228 tok/sNVIDIA TITAN RTX~228 tok/sNVIDIA GeForce RTX 2080 Ti~209 tok/sNVIDIA GeForce RTX 3070 Ti~206 tok/sNVIDIA GeForce RTX 4090 Laptop GPU~195 tok/sAMD Radeon RX 9070~183 tok/sAMD Radeon RX 9070 XT~183 tok/sAMD Radeon RX 7800 XT~179 tok/sNVIDIA GeForce RTX 4070~171 tok/sNVIDIA GeForce RTX 4070 SUPER~171 tok/sNVIDIA GeForce RTX 4070 Ti~171 tok/sAMD Radeon RX 7900 GRE~165 tok/sNVIDIA GeForce GTX 1080 Ti~164 tok/sNVIDIA GeForce RTX 3060 Ti~152 tok/sNVIDIA GeForce RTX 3070~152 tok/sNVIDIA GeForce RTX 5060~152 tok/sNVIDIA GeForce RTX 5060 Ti 16GB~152 tok/sNVIDIA GeForce RTX 5060 Ti 8GB~152 tok/sAMD Radeon RX 6800~147 tok/sAMD Radeon RX 6800 XT~147 tok/sAMD Radeon RX 6900 XT~147 tok/sIntel Arc A770 16GB~146 tok/sIntel Arc A750~133 tok/sAMD Radeon RX 7700 XT~124 tok/sNVIDIA GeForce RTX 3060 12GB~122 tok/sIntel Arc B580~119 tok/sAMD Radeon RX 6700 XT~110 tok/sIntel Arc B570~99 tok/sNVIDIA GeForce RTX 4060 Ti 16GB~98 tok/sNVIDIA GeForce RTX 4060 Ti 8GB~98 tok/sNVIDIA GeForce RTX 4060~92 tok/sAMD Radeon RX 9060 XT 16GB~92 tok/sAMD Radeon RX 7600~83 tok/sAMD Radeon RX 7600 XT~83 tok/sNVIDIA GeForce RTX 3060 8GB~81 tok/sNVIDIA GeForce RTX 3050 8GB~76 tok/s

Which Devices Can Run EuroMoE 2.6B A0.6B 2512?

Q4_K_M · 1.9 GB

59 devices with unified memory can run EuroMoE 2.6B A0.6B 2512, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Runs great

Plenty of headroom
NVIDIA DGX H100~9073 tok/sNVIDIA DGX A100 640GB~5522 tok/sMac Studio (M3 Ultra, 256GB)~299 tok/sMac Studio (M3 Ultra, 512GB)~299 tok/sMac Studio (M3 Ultra, 96GB)~299 tok/sMac Pro M2 Ultra (192 GB)~292 tok/sMac Studio M2 Ultra (192 GB)~292 tok/sMacBook Pro 16" M5 Max (128 GB)~224 tok/sMac Studio M4 Max (128 GB)~199 tok/sMac Studio M4 Max (64 GB)~199 tok/sMacBook Pro 16" M4 Max (48 GB)~199 tok/sMacBook Pro 16" M4 Max (64 GB)~199 tok/sMac Studio M4 Max (36 GB)~149 tok/sMacBook Pro 14" M4 Max (36 GB)~149 tok/sMacBook Pro 16" M3 Max (48 GB)~149 tok/sMacBook Pro 14-inch (M5 Pro)~112 tok/sMac Mini M4 Pro (24 GB)~100 tok/sMac Mini M4 Pro (48 GB)~100 tok/sMacBook Pro 14" M4 Pro (24 GB)~100 tok/sMacBook Pro 16" M4 Pro (24 GB)~100 tok/sASUS Ascent GX10~92 tok/sNVIDIA DGX Spark~92 tok/sNVIDIA Jetson AGX Thor Developer Kit~92 tok/sAsus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB)~87 tok/sBeelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB)~87 tok/sFramework Desktop (Ryzen AI Max+ 395, 128 GB)~87 tok/sGMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB)~87 tok/sHP Z2 Mini G1a (Ryzen AI Max+ PRO 395, 128 GB)~87 tok/sHP ZBook Ultra G1a 14 (Ryzen AI Max+ PRO 395, 128 GB)~87 tok/sMinisforum MS-S1 MAX (Ryzen AI Max+ 395, 128 GB)~87 tok/sSnapdragon X2 Elite Extreme Copilot+ PC~77 tok/sNVIDIA Jetson AGX Orin 32GB~69 tok/sNVIDIA Jetson AGX Orin 64GB~69 tok/sMacBook Pro 14-inch (M5)~56 tok/siPad Pro M5 13" (16 GB)~56 tok/sSnapdragon X Elite Copilot+ PC~46 tok/sMac Mini M4 (16 GB)~44 tok/sMac Mini M4 (32 GB)~44 tok/sMacBook Air 13" M4 (16 GB)~44 tok/sMacBook Air 13" M4 (24 GB)~44 tok/sMacBook Air 15" M4 (16 GB)~44 tok/sMacBook Air 15" M4 (24 GB)~44 tok/sMacBook Pro 14" M4 (16 GB)~44 tok/siPad Pro M4 13" (16 GB)~44 tok/sMacBook Air 13" M3 (16 GB)~37 tok/sMacBook Air 13" M3 (24 GB)~37 tok/sMacBook Air 13" M3 (8 GB)~37 tok/sIntel Core Ultra 9 288V (Lunar Lake) Laptop~36 tok/sNVIDIA Jetson Orin NX 16GB~35 tok/sNVIDIA Jetson Orin Nano 8GB (Super)~35 tok/sAMD Ryzen AI 9 HX 370 (Strix Point) Laptop~34 tok/sApple iPhone 17 Pro~28 tok/siPhone 17 Pro Max~28 tok/siPhone 17~25 tok/siPhone Air~25 tok/siPhone 15 ProiPhone 15 Pro MaxiPhone 16 ProiPhone 16 Pro Max

Related Models

Frequently Asked Questions

How much VRAM does EuroMoE 2.6B A0.6B 2512 need?

EuroMoE 2.6B A0.6B 2512 requires 1.9 GB of VRAM at Q4_K_M, or 5.6 GB at BF16.

VRAM = Weights + KV Cache + Overhead

Weights = 2.6B × 4.8 bits ÷ 8 = 1.6 GB

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

KV Cache + Overhead 0.4 GB (at full 4K context)

VRAM usage by quantization

1.9 GB
2.0 GB

Learn more about VRAM estimation →

What's the best quantization for EuroMoE 2.6B A0.6B 2512?

For EuroMoE 2.6B A0.6B 2512, Q4_K_M (1.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.5 GB.

VRAM requirement by quantization

Q2_K
1.5 GB
Q4_K_M
1.9 GB
Q5_K_M
2.2 GB
Q6_K
2.5 GB
Q8_0
3.0 GB
BF16
5.6 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run EuroMoE 2.6B A0.6B 2512 on a Mac?

EuroMoE 2.6B A0.6B 2512 requires at least 1.5 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 EuroMoE 2.6B A0.6B 2512 locally?

Yes — EuroMoE 2.6B A0.6B 2512 can run locally on consumer hardware. At Q4_K_M quantization it needs 1.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is EuroMoE 2.6B A0.6B 2512?

At Q4_K_M, EuroMoE 2.6B A0.6B 2512 can reach ~2292 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~341 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 ÷ 1.9 × 0.65 = ~2708 tok/s

Estimated speed at Q4_K_M (1.9 GB)

~2708 tok/s
~341 tok/s
~2708 tok/s
~2292 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 EuroMoE 2.6B A0.6B 2512?

At Q4_K_M, the download is about 1.57 GB. The full-precision BF16 version is 5.22 GB. The smallest option (Q2_K) is 1.11 GB.

Which GPUs can run EuroMoE 2.6B A0.6B 2512?

50 consumer GPUs can run EuroMoE 2.6B A0.6B 2512 at Q4_K_M (1.9 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 EuroMoE 2.6B A0.6B 2512?

59 devices with unified memory can run EuroMoE 2.6B A0.6B 2512 at Q4_K_M (1.9 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.