Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF — Hardware Requirements & GPU Compatibility
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15 downloadsSpecifications
- Publisher
- s3nh
- Family
- Llama 2
- Parameters
- 7B
- License
- openrail
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How Much VRAM Does Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.3 GB | — | 2.98 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.4 GB | — | 3.06 GB | 3-bit small quantization |
| Q4_K_S | 4.50 | 4.3 GB | — | 3.94 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 4.6 GB | — | 4.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 5.3 GB | — | 4.81 GB | 5-bit small quantization |
| Q6_K | 6.60 | 6.3 GB | — | 5.78 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 7.7 GB | — | 7.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF?
Q4_K_M · 4.6 GBHunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF (Q4_K_M) requires 4.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ 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 Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF?
Q4_K_M · 4.6 GB33 devices with unified memory can run Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF need?
Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF requires 4.6 GB of VRAM at Q4_K_M, or 7.7 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 4.8 bits ÷ 8 = 4.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M4.6 GB- What's the best quantization for Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF?
For Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF, Q4_K_M (4.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (5.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.3 GB.
VRAM requirement by quantization
Q2_K3.3 GB~75%Q4_K_S4.3 GB~88%Q4_K_M ★4.6 GB~89%Q5_K_S5.3 GB~92%Q6_K6.3 GB~95%Q8_07.7 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF on a Mac?
Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF requires at least 3.3 GB at Q2_K, 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 Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF locally?
Yes — Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 4.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF?
At Q4_K_M, Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF can reach ~631 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~142 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 ÷ 4.6 × 0.55 = ~631 tok/s
Estimated speed at Q4_K_M (4.6 GB)
AMD Instinct MI300X~631 tok/sNVIDIA GeForce RTX 4090~142 tok/sNVIDIA H100 SXM~472 tok/sAMD Instinct MI250X~390 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF?
At Q4_K_M, the download is about 4.20 GB. The full-precision Q8_0 version is 7.00 GB. The smallest option (Q2_K) is 2.98 GB.
- Which GPUs can run Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF?
35 consumer GPUs can run Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF at Q4_K_M (4.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF?
33 devices with unified memory can run Hunkim NousResearch Llama 2 7B HF Ko 7 Koalpaca V1.1a Kopen Platypus GGUF at Q4_K_M (4.6 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.