Qmd Query Expansion 1.7B GGUF — Hardware Requirements & GPU Compatibility
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60.7K downloads
13 likesSpecifications
- Publisher
- tobil
- Parameters
- 1.7B
- Release Date
- 2026-01-29
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Qmd Query Expansion 1.7B GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 1.1 GB | — | 1.02 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.3 GB | — | 1.21 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q8_0 | 8.00 | 1.9 GB | — | 1.70 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qmd Query Expansion 1.7B GGUF?
Q4_K_M · 1.1 GBQmd Query Expansion 1.7B GGUF (Q4_K_M) requires 1.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qmd Query Expansion 1.7B GGUF?
Q4_K_M · 1.1 GB33 devices with unified memory can run Qmd Query Expansion 1.7B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qmd Query Expansion 1.7B GGUF need?
Qmd Query Expansion 1.7B GGUF requires 1.1 GB of VRAM at Q4_K_M, or 1.9 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 1.7B × 4.8 bits ÷ 8 = 1 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.1 GB- What's the best quantization for Qmd Query Expansion 1.7B GGUF?
For Qmd Query Expansion 1.7B GGUF, Q4_K_M (1.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.3 GB) provides better quality if you have the VRAM.
VRAM requirement by quantization
Q4_K_M ★1.1 GB~89%Q5_K_M1.3 GB~92%Q8_01.9 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qmd Query Expansion 1.7B GGUF on a Mac?
Qmd Query Expansion 1.7B GGUF requires at least 1.1 GB at Q4_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 Qmd Query Expansion 1.7B GGUF locally?
Yes — Qmd Query Expansion 1.7B GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 1.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qmd Query Expansion 1.7B GGUF?
At Q4_K_M, Qmd Query Expansion 1.7B GGUF can reach ~2603 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~585 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 ÷ 1.1 × 0.55 = ~2603 tok/s
Estimated speed at Q4_K_M (1.1 GB)
AMD Instinct MI300X~2603 tok/sNVIDIA GeForce RTX 4090~585 tok/sNVIDIA H100 SXM~1945 tok/sAMD Instinct MI250X~1609 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qmd Query Expansion 1.7B GGUF?
At Q4_K_M, the download is about 1.02 GB. The full-precision Q8_0 version is 1.70 GB.