Cydonia 24B V4.3 — Hardware Requirements & GPU Compatibility
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Cydonia 24B V4.3 is a 23.6B-parameter open language model from TheDrummer. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 14.86 GB of VRAM — see which GPUs and Macs can run it below.
6.0K downloads
118 likesSpecifications
- Publisher
- TheDrummer
- Parameters
- 23.6B
- Architecture
- MistralForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2025-11-08
Get Started
HuggingFace
How Much VRAM Does Cydonia 24B V4.3 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 10.7 GB | 37.2 GB | 10.02 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 11.0 GB | 37.5 GB | 10.31 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 12.2 GB | 38.6 GB | 11.49 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 12.5 GB | 38.9 GB | 11.79 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 14.9 GB | 41.3 GB | 14.14 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 17.5 GB | 43.9 GB | 16.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 20.2 GB | 46.6 GB | 19.45 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 24.3 GB | 50.7 GB | 23.57 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Cydonia 24B V4.3?
Q4_K_M · 14.9 GBCydonia 24B V4.3 (Q4_K_M) requires 14.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. Using the full 131K context window can add up to 26.4 GB, bringing total usage to 41.3 GB. 17 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Cydonia 24B V4.3?
Q4_K_M · 14.9 GB27 devices with unified memory can run Cydonia 24B V4.3, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomWhere to Download Cydonia 24B V4.3
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Cydonia 24B V4.3 need?
Cydonia 24B V4.3 requires 14.9 GB of VRAM at Q4_K_M, or 47.9 GB at BF16. Full 131K context adds up to 26.4 GB (41.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 23.6B × 4.8 bits ÷ 8 = 14.1 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 27.2 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M14.9 GBQ4_K_M + full context41.3 GB- Can NVIDIA GeForce RTX 4090 run Cydonia 24B V4.3?
Yes, at Q6_K (20.2 GB) or lower. Higher quantizations like Q8_0 (24.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Cydonia 24B V4.3?
For Cydonia 24B V4.3, Q4_K_M (14.9 GB) offers the best balance of quality and VRAM usage. Q4_K_L (15.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 7.8 GB.
VRAM requirement by quantization
IQ2_XS7.8 GBQ3_K_S11.0 GBIQ4_XS13.4 GBQ4_K_M ★14.9 GBQ5_K_S16.9 GBBF1647.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Cydonia 24B V4.3 on a Mac?
Cydonia 24B V4.3 requires at least 7.8 GB at IQ2_XS, 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 24B V4.3 locally?
Yes — Cydonia 24B V4.3 can run locally on consumer hardware. At Q4_K_M quantization it needs 14.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Cydonia 24B V4.3?
At Q4_K_M, Cydonia 24B V4.3 can reach ~196 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~44 tok/s. 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 ÷ 14.9 × 0.55 = ~196 tok/s
Estimated speed at Q4_K_M (14.9 GB)
~196 tok/s~44 tok/s~147 tok/s~121 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Cydonia 24B V4.3?
At Q4_K_M, the download is about 14.14 GB. The full-precision BF16 version is 47.14 GB. The smallest option (IQ2_XS) is 7.07 GB.
- Which GPUs can run Cydonia 24B V4.3?
17 consumer GPUs can run Cydonia 24B V4.3 at Q4_K_M (14.9 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 5 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Cydonia 24B V4.3?
27 devices with unified memory can run Cydonia 24B V4.3 at Q4_K_M (14.9 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.