CausalLM 14B GGUF — Hardware Requirements & GPU Compatibility
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7.9K downloads
193 likesSpecifications
- Publisher
- TheBloke
- Parameters
- 14B
- Release Date
- 2023-10-23
- License
- WTFPL
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HuggingFace
How Much VRAM Does CausalLM 14B GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_0 | 4.00 | 7.7 GB | — | 7.00 GB | 4-bit legacy quantization |
| Q4_1 | 4.50 | 8.7 GB | — | 7.88 GB | 4-bit legacy quantization with offset |
| Q5_0 | 5.00 | 9.6 GB | — | 8.75 GB | 5-bit legacy quantization |
| Q5_1 | 5.50 | 10.6 GB | — | 9.63 GB | 5-bit legacy quantization with offset |
| Q8_0 | 8.00 | 15.4 GB | — | 14.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run CausalLM 14B GGUF?
Q4_0 · 7.7 GBCausalLM 14B GGUF (Q4_0) requires 7.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run CausalLM 14B GGUF?
Q4_0 · 7.7 GB33 devices with unified memory can run CausalLM 14B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does CausalLM 14B GGUF need?
CausalLM 14B GGUF requires 7.7 GB of VRAM at Q4_0, or 15.4 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 14B × 4 bits ÷ 8 = 7 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_07.7 GB- What's the best quantization for CausalLM 14B GGUF?
For CausalLM 14B GGUF, Q5_0 (9.6 GB) offers the best balance of quality and VRAM usage. Q5_1 (10.6 GB) provides better quality if you have the VRAM. The smallest option is Q4_0 at 7.7 GB.
VRAM requirement by quantization
Q4_07.7 GB~85%Q4_18.7 GB~88%Q5_0 ★9.6 GB~90%Q5_110.6 GB~92%Q8_015.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run CausalLM 14B GGUF on a Mac?
CausalLM 14B GGUF requires at least 7.7 GB at Q4_0, 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 GGUF locally?
Yes — CausalLM 14B GGUF can run locally on consumer hardware. At Q4_0 quantization it needs 7.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is CausalLM 14B GGUF?
At Q4_0, CausalLM 14B GGUF can reach ~379 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~85 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 ÷ 7.7 × 0.55 = ~379 tok/s
Estimated speed at Q4_0 (7.7 GB)
AMD Instinct MI300X~379 tok/sNVIDIA GeForce RTX 4090~85 tok/sNVIDIA H100 SXM~283 tok/sAMD Instinct MI250X~234 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 GGUF?
At Q4_0, the download is about 7.00 GB. The full-precision Q8_0 version is 14.00 GB.
- Which GPUs can run CausalLM 14B GGUF?
35 consumer GPUs can run CausalLM 14B GGUF at Q4_0 (7.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run CausalLM 14B GGUF?
33 devices with unified memory can run CausalLM 14B GGUF at Q4_0 (7.7 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.