Goekdeniz-Guelmez·Qwen3ForCausalLM

JOSIE 1.1 4B Instruct — Hardware Requirements & GPU Compatibility

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JOSIE 1.1 4B Instruct is a 4.0B-parameter open language model from Goekdeniz-Guelmez. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 2.90 GB of VRAM — see which GPUs and Macs can run it below.

280 downloads 2 likes262K context

Specifications

Publisher
Goekdeniz-Guelmez
Parameters
4.0B
Architecture
Qwen3ForCausalLM
Context Length
262,144 tokens
Vocabulary Size
151,669
Release Date
2026-03-08
License
MIT

Get Started

How Much VRAM Does JOSIE 1.1 4B Instruct Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.402.2 GB
Q3_K_Mest.3.902.5 GB
Q4_K_Mest.4.802.9 GB
Q5_K_Mest.5.703.4 GB
Q6_Kest.6.603.8 GB
Q8_0est.8.004.5 GB
BF16est.16.008.5 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 JOSIE 1.1 4B Instruct?

Q4_K_M · 2.9 GB

JOSIE 1.1 4B Instruct (Q4_K_M) requires 2.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 262K context window can add up to 24.0 GB, bringing total usage to 26.9 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

Plenty of headroom
NVIDIA GeForce RTX 5090~402 tok/sNVIDIA GeForce RTX 3090 Ti~226 tok/sNVIDIA GeForce RTX 4090~226 tok/sNVIDIA GeForce RTX 5080~215 tok/sNVIDIA GeForce RTX 3090~210 tok/sNVIDIA GeForce RTX 3080 Ti~205 tok/sNVIDIA GeForce RTX 5070 Ti~201 tok/sNVIDIA GeForce RTX 5090 Laptop GPU~201 tok/sAMD Radeon RX 7900 XTX~182 tok/sNVIDIA GeForce RTX 3080~170 tok/sNVIDIA GeForce RTX 4080 SUPER~165 tok/sNVIDIA GeForce RTX 4080~161 tok/sAMD Radeon RX 7900 XT~152 tok/sNVIDIA GeForce RTX 4070 Ti SUPER~151 tok/sNVIDIA GeForce RTX 5070~151 tok/sNVIDIA TITAN RTX~151 tok/sNVIDIA GeForce RTX 2080 Ti~138 tok/sNVIDIA GeForce RTX 3070 Ti~136 tok/sNVIDIA GeForce RTX 4090 Laptop GPU~129 tok/sAMD Radeon RX 9070~121 tok/sAMD Radeon RX 9070 XT~121 tok/sAMD Radeon RX 7800 XT~118 tok/sNVIDIA GeForce RTX 4070~113 tok/sNVIDIA GeForce RTX 4070 SUPER~113 tok/sNVIDIA GeForce RTX 4070 Ti~113 tok/sAMD Radeon RX 7900 GRE~109 tok/sNVIDIA GeForce GTX 1080 Ti~109 tok/sNVIDIA GeForce RTX 3060 Ti~100 tok/sNVIDIA GeForce RTX 3070~100 tok/sNVIDIA GeForce RTX 5060~100 tok/sNVIDIA GeForce RTX 5060 Ti 16GB~100 tok/sNVIDIA GeForce RTX 5060 Ti 8GB~100 tok/sAMD Radeon RX 6800~97 tok/sAMD Radeon RX 6800 XT~97 tok/sAMD Radeon RX 6900 XT~97 tok/sIntel Arc A770 16GB~97 tok/sIntel Arc A750~88 tok/sAMD Radeon RX 7700 XT~82 tok/sNVIDIA GeForce RTX 3060 12GB~81 tok/sIntel Arc B580~79 tok/sAMD Radeon RX 6700 XT~73 tok/sIntel Arc B570~66 tok/sNVIDIA GeForce RTX 4060 Ti 16GB~65 tok/sNVIDIA GeForce RTX 4060 Ti 8GB~65 tok/sNVIDIA GeForce RTX 4060~61 tok/sAMD Radeon RX 9060 XT 16GB~61 tok/sAMD Radeon RX 7600~55 tok/sAMD Radeon RX 7600 XT~55 tok/sNVIDIA GeForce RTX 3060 8GB~54 tok/sNVIDIA GeForce RTX 3050 8GB~50 tok/s

Which Devices Can Run JOSIE 1.1 4B Instruct?

Q4_K_M · 2.9 GB

59 devices with unified memory can run JOSIE 1.1 4B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Runs great

Plenty of headroom
NVIDIA DGX H100~6007 tok/sNVIDIA DGX A100 640GB~3656 tok/sMac Studio (M3 Ultra, 256GB)~198 tok/sMac Studio (M3 Ultra, 512GB)~198 tok/sMac Studio (M3 Ultra, 96GB)~198 tok/sMac Pro M2 Ultra (192 GB)~193 tok/sMac Studio M2 Ultra (192 GB)~193 tok/sMacBook Pro 16" M5 Max (128 GB)~148 tok/sMac Studio M4 Max (128 GB)~132 tok/sMac Studio M4 Max (64 GB)~132 tok/sMacBook Pro 16" M4 Max (48 GB)~132 tok/sMacBook Pro 16" M4 Max (64 GB)~132 tok/sMac Studio M4 Max (36 GB)~99 tok/sMacBook Pro 14" M4 Max (36 GB)~99 tok/sMacBook Pro 16" M3 Max (48 GB)~99 tok/sMacBook Pro 14-inch (M5 Pro)~74 tok/sMac Mini M4 Pro (24 GB)~66 tok/sMac Mini M4 Pro (48 GB)~66 tok/sMacBook Pro 14" M4 Pro (24 GB)~66 tok/sMacBook Pro 16" M4 Pro (24 GB)~66 tok/sASUS Ascent GX10~61 tok/sNVIDIA DGX Spark~61 tok/sNVIDIA Jetson AGX Thor Developer Kit~61 tok/sAsus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB)~57 tok/sBeelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB)~57 tok/sFramework Desktop (Ryzen AI Max+ 395, 128 GB)~57 tok/sGMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB)~57 tok/sHP Z2 Mini G1a (Ryzen AI Max+ PRO 395, 128 GB)~57 tok/sHP ZBook Ultra G1a 14 (Ryzen AI Max+ PRO 395, 128 GB)~57 tok/sMinisforum MS-S1 MAX (Ryzen AI Max+ 395, 128 GB)~57 tok/sSnapdragon X2 Elite Extreme Copilot+ PC~51 tok/sNVIDIA Jetson AGX Orin 32GB~46 tok/sNVIDIA Jetson AGX Orin 64GB~46 tok/sMacBook Pro 14-inch (M5)~37 tok/siPad Pro M5 13" (16 GB)~37 tok/sSnapdragon X Elite Copilot+ PC~30 tok/sMac Mini M4 (16 GB)~29 tok/sMac Mini M4 (32 GB)~29 tok/sMacBook Air 13" M4 (16 GB)~29 tok/sMacBook Air 13" M4 (24 GB)~29 tok/sMacBook Air 15" M4 (16 GB)~29 tok/sMacBook Air 15" M4 (24 GB)~29 tok/sMacBook Pro 14" M4 (16 GB)~29 tok/siPad Pro M4 13" (16 GB)~29 tok/sMacBook Air 13" M3 (16 GB)~25 tok/sMacBook Air 13" M3 (24 GB)~25 tok/sMacBook Air 13" M3 (8 GB)~25 tok/sIntel Core Ultra 9 288V (Lunar Lake) Laptop~24 tok/sNVIDIA Jetson Orin NX 16GB~23 tok/sNVIDIA Jetson Orin Nano 8GB (Super)~23 tok/sAMD Ryzen AI 9 HX 370 (Strix Point) Laptop~23 tok/sApple iPhone 17 Pro~19 tok/siPhone 17 Pro Max~19 tok/siPhone 17~17 tok/siPhone Air~17 tok/siPhone 15 ProiPhone 15 Pro MaxiPhone 16 ProiPhone 16 Pro Max

Related Models

Frequently Asked Questions

How much VRAM does JOSIE 1.1 4B Instruct need?

JOSIE 1.1 4B Instruct requires 2.9 GB of VRAM at Q4_K_M, or 8.5 GB at BF16. Full 262K context adds up to 24.0 GB (26.9 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 4.0B × 4.8 bits ÷ 8 = 2.4 GB

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

KV Cache + Overhead 24.5 GB (at full 262K context)

VRAM usage by quantization

2.9 GB
26.9 GB

Learn more about VRAM estimation →

What's the best quantization for JOSIE 1.1 4B Instruct?

For JOSIE 1.1 4B Instruct, Q4_K_M (2.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.2 GB.

VRAM requirement by quantization

Q2_K
2.2 GB
Q4_K_M
2.9 GB
Q5_K_M
3.4 GB
Q6_K
3.8 GB
Q8_0
4.5 GB
BF16
8.5 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run JOSIE 1.1 4B Instruct on a Mac?

JOSIE 1.1 4B Instruct requires at least 2.2 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 JOSIE 1.1 4B Instruct locally?

Yes — JOSIE 1.1 4B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 2.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is JOSIE 1.1 4B Instruct?

At Q4_K_M, JOSIE 1.1 4B Instruct can reach ~1517 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~226 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 ÷ 2.9 × 0.65 = ~1793 tok/s

Estimated speed at Q4_K_M (2.9 GB)

~1793 tok/s
~226 tok/s
~1793 tok/s
~1517 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 JOSIE 1.1 4B Instruct?

At Q4_K_M, the download is about 2.41 GB. The full-precision BF16 version is 8.04 GB. The smallest option (Q2_K) is 1.71 GB.

Which GPUs can run JOSIE 1.1 4B Instruct?

50 consumer GPUs can run JOSIE 1.1 4B Instruct at Q4_K_M (2.9 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 JOSIE 1.1 4B Instruct?

59 devices with unified memory can run JOSIE 1.1 4B Instruct at Q4_K_M (2.9 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.