CausalLM 14B DPO Alpha GGUF — Hardware Requirements & GPU Compatibility
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3.1K downloads
98 likesSpecifications
- Publisher
- tastypear
- Parameters
- 14B
- Release Date
- 2023-11-25
- License
- WTFPL
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HuggingFace
How Much VRAM Does CausalLM 14B DPO Alpha GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q3_K_M | 3.90 | 7.5 GB | — | 6.83 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 9.2 GB | — | 8.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 10.6 GB | — | 9.63 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 11.0 GB | — | 9.97 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 12.7 GB | — | 11.55 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 15.4 GB | — | 14.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run CausalLM 14B DPO Alpha GGUF?
Q4_K_M · 9.2 GBCausalLM 14B DPO Alpha GGUF (Q4_K_M) requires 9.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run CausalLM 14B DPO Alpha GGUF?
Q4_K_M · 9.2 GB27 devices with unified memory can run CausalLM 14B DPO Alpha GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does CausalLM 14B DPO Alpha GGUF need?
CausalLM 14B DPO Alpha GGUF requires 9.2 GB of VRAM at Q4_K_M, or 15.4 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 14B × 4.8 bits ÷ 8 = 8.4 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M9.2 GB- What's the best quantization for CausalLM 14B DPO Alpha GGUF?
For CausalLM 14B DPO Alpha GGUF, Q4_K_M (9.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (10.6 GB) provides better quality if you have the VRAM. The smallest option is Q3_K_M at 7.5 GB.
VRAM requirement by quantization
Q3_K_M7.5 GB~83%Q4_K_M ★9.2 GB~89%Q5_K_S10.6 GB~92%Q5_K_M11.0 GB~92%Q6_K12.7 GB~95%Q8_015.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run CausalLM 14B DPO Alpha GGUF on a Mac?
CausalLM 14B DPO Alpha GGUF requires at least 7.5 GB at Q3_K_M, 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 CausalLM 14B DPO Alpha GGUF locally?
Yes — CausalLM 14B DPO Alpha GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 9.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is CausalLM 14B DPO Alpha GGUF?
At Q4_K_M, CausalLM 14B DPO Alpha GGUF can reach ~316 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~71 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 ÷ 9.2 × 0.55 = ~316 tok/s
Estimated speed at Q4_K_M (9.2 GB)
AMD Instinct MI300X~316 tok/sNVIDIA GeForce RTX 4090~71 tok/sNVIDIA H100 SXM~236 tok/sAMD Instinct MI250X~195 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of CausalLM 14B DPO Alpha GGUF?
At Q4_K_M, the download is about 8.40 GB. The full-precision Q8_0 version is 14.00 GB. The smallest option (Q3_K_M) is 6.83 GB.
- Which GPUs can run CausalLM 14B DPO Alpha GGUF?
28 consumer GPUs can run CausalLM 14B DPO Alpha GGUF at Q4_K_M (9.2 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run CausalLM 14B DPO Alpha GGUF?
27 devices with unified memory can run CausalLM 14B DPO Alpha GGUF at Q4_K_M (9.2 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.