Apodex 1.0 Mini — Hardware Requirements & GPU Compatibility
ChatFunctions
Apodex 1.0 Mini is a 36.0B-parameter open language model from apodex. It supports a context window of up to 262,144 tokens. At BF16 it needs about 72.29 GB of VRAM — see which GPUs and Macs can run it below.
Apodex 1.0 Mini runs on a 96 GB+ Mac or a workstation GPU
Best local pick → Mac Studio (M3 Ultra, 96GB) · BF16 · 72.4 GB · ~41 tok/s
Macs from 96 GB (BF16 · ~41 tok/s)
Apache 2.0
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Apodex 1.0 Mini
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve apodex/Apodex-1.0-mini --max-model-len 16384 --tensor-parallel-size 8vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX PRO 6000 (96 GB) (23 GB headroom) · BF16
- Generation speed
- ~96 tok/s
- generation speed
- Cost per 1M output tokens
- $3.87
- per 1M output tokens
How Much VRAM Does Apodex 1.0 Mini Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 72.3 GB | 72.5 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on H100 SXM 80 GB |
Selected BF16
Brain floating point 1672.4GBat 16K context
- Weights
- 71.9 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Server-class: ~72.4 GB at 16K context · one H100 SXM 80 GB
Which Hardware Can Run Apodex 1.0 Mini?
BF16 · 72.3 GBApodex 1.0 Mini (BF16) requires 72.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 94+ GB is recommended. Using the full 262K context window can add up to 2.6 GB, bringing total usage to 74.9 GB. No consumer GPU has enough memory. 30 devices with unified memory can run Apodex 1.0 Mini, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 96GB).
96 GB
fits · 23.6 GB free · up to 256K ctx
3 GPUs · 2 Mac classes
RTX PRO 6000 Blackwell Max-Q Workstation Edition~96Simulate output on RTX PRO 6000 Blackwell Max-Q Workstation EditionRTX PRO 6000 Blackwell Workstation Edition~96Simulate output on RTX PRO 6000 Blackwell Workstation EditionRTX PRO 6000 Blackwell Server Edition~88Simulate output on RTX PRO 6000 Blackwell Server EditionM3 Ultra*96 GB~41Simulate output on Mac Studio (M3 Ultra, 96GB)M3 Max*96 GB~19Simulate output on MacBook Pro 14" M3 Max (96 GB)128–144 GB
fits · 55.6 GB free · up to 256K ctx
4 GPUs · 2 Mac classes · 4 device families
GH200 Grace Hopper Superchip~192Simulate output on GH200 Grace Hopper SuperchipH200 NVL~190Simulate output on H200 NVLH200 SXM~190Simulate output on H200 SXMM2 Ultra*128 GB~40Simulate output on Mac Pro M2 Ultra (128 GB)M5 Max128 GB~33Simulate output on Mac Studio M5 Max (128 GB)GB10~18Simulate output on NVIDIA DGX SparkJetson T5000 (Thor)~18Simulate output on NVIDIA Jetson AGX Thor Developer Kit
By VRAM tier · typical GPUs and Macs at BF16
Consumer GPUs (up to 32 GB) and Macs up to 64 GB: too small for this model (needs ≥ 72.4 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.6 35B A3BAlibaba | 106.8 | 22 GB | ~151 | same size |
| MiniCPM V 4.7 35B A3BOpenBMB | — | 22.1 GB | ~169 | same size |
| Ornith 1.5 35B A3Bornith-ai | — | 22 GB | ~151 | same size |
| Agents A1InternScience | — | 21.5 GB | ~171 | same size |
| Intern S2 PreviewInternLM | — | 73.2 GB | —needs > 24 GB | same size |
| EXAONE 4.5 33BLGAI-EXAONE | — | 23.9 GB | ~33 | same size |
Specifications
- Publisher
- apodex
- Parameters
- 36.0B
- Active parameters
- 4.7B per token
- Architecture
- Qwen3_5MoeForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-06-07
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Apodex 1.0 Mini need?
Apodex 1.0 Mini requires 72.3 GB of VRAM at BF16. Full 262K context adds up to 2.6 GB (74.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 36.0B × 16 bits ÷ 8 = 71.9 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 3 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run Apodex 1.0 Mini?
No — Apodex 1.0 Mini requires at least 72.3 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
Can I run Apodex 1.0 Mini on a Mac?
Yes — Mac Studio (M3 Ultra, 96GB) and 20 other Macs can run Apodex 1.0 Mini. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 72.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Apodex 1.0 Mini locally?
Yes — Apodex 1.0 Mini can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Apodex 1.0 Mini?
At BF16, Apodex 1.0 Mini can reach ~100 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 9.5 GB active ÷ (8000 × 0.65) = 1.84 ms, plus 40 layers × 0.055 ms = 2.20 ms, so 1000 ÷ 4.04 ms = ~248 tok/s
Estimated speed at BF16 (72.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Apodex 1.0 Mini?
At BF16, the download is about 71.90 GB.
Which GPUs can run Apodex 1.0 Mini?
No single consumer GPU has enough VRAM to run Apodex 1.0 Mini at BF16 (72.3 GB). Multi-GPU or professional hardware is required.
Which devices can run Apodex 1.0 Mini?
30 devices with unified memory can run Apodex 1.0 Mini at BF16 (72.3 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.