Cydonia ForgottenThoughts 24B — Hardware Requirements & GPU Compatibility
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2 downloads
5 likesSpecifications
- Publisher
- Casual-Autopsy
- Parameters
- 23.6B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2025-03-29
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HuggingFace
How Much VRAM Does Cydonia ForgottenThoughts 24B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 47.9 GB | 54.2 GB | 47.14 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Cydonia ForgottenThoughts 24B?
BF16 · 47.9 GBCydonia ForgottenThoughts 24B (BF16) requires 47.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 63+ GB is recommended. Using the full 33K context window can add up to 6.3 GB, bringing total usage to 54.2 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Cydonia ForgottenThoughts 24B?
BF16 · 47.9 GB11 devices with unified memory can run Cydonia ForgottenThoughts 24B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does Cydonia ForgottenThoughts 24B need?
Cydonia ForgottenThoughts 24B requires 47.9 GB of VRAM at BF16. Full 33K context adds up to 6.3 GB (54.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 23.6B × 16 bits ÷ 8 = 47.1 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7.1 GB (at full 33K context)
VRAM usage by quantization
BF1647.9 GBBF16 + full context54.2 GB- Can NVIDIA GeForce RTX 5090 run Cydonia ForgottenThoughts 24B?
No — Cydonia ForgottenThoughts 24B requires at least 47.9 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Cydonia ForgottenThoughts 24B on a Mac?
Cydonia ForgottenThoughts 24B requires at least 47.9 GB at BF16, 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 Cydonia ForgottenThoughts 24B locally?
Yes — Cydonia ForgottenThoughts 24B can run locally on consumer hardware. At BF16 quantization it needs 47.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Cydonia ForgottenThoughts 24B?
At BF16, Cydonia ForgottenThoughts 24B can reach ~61 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 47.9 × 0.55 = ~61 tok/s
Estimated speed at BF16 (47.9 GB)
AMD Instinct MI300X~61 tok/sNVIDIA H100 SXM~46 tok/sAMD Instinct MI250X~38 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Cydonia ForgottenThoughts 24B?
At BF16, the download is about 47.14 GB.
- Which GPUs can run Cydonia ForgottenThoughts 24B?
No single consumer GPU has enough VRAM to run Cydonia ForgottenThoughts 24B at BF16 (47.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run Cydonia ForgottenThoughts 24B?
11 devices with unified memory can run Cydonia ForgottenThoughts 24B at BF16 (47.9 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.