Isotonic.OrcaAgent Llama3.2 1B GGUF — Hardware Requirements & GPU Compatibility
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36 downloads
2 likesSpecifications
- Publisher
- DevQuasar-5
- Family
- Llama 3
- Parameters
- 1B
- Release Date
- 2025-02-01
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How Much VRAM Does Isotonic.OrcaAgent Llama3.2 1B GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.5 GB | — | 0.42 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 0.5 GB | — | 0.49 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 0.7 GB | — | 0.60 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.8 GB | — | 0.71 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.9 GB | — | 0.82 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.1 GB | — | 1.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Isotonic.OrcaAgent Llama3.2 1B GGUF?
Q4_K_M · 0.7 GBIsotonic.OrcaAgent Llama3.2 1B GGUF (Q4_K_M) requires 0.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ 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 Isotonic.OrcaAgent Llama3.2 1B GGUF?
Q4_K_M · 0.7 GB33 devices with unified memory can run Isotonic.OrcaAgent Llama3.2 1B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Isotonic.OrcaAgent Llama3.2 1B GGUF need?
Isotonic.OrcaAgent Llama3.2 1B GGUF requires 0.7 GB of VRAM at Q4_K_M, or 1.1 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 1B × 4.8 bits ÷ 8 = 0.6 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M0.7 GB- What's the best quantization for Isotonic.OrcaAgent Llama3.2 1B GGUF?
For Isotonic.OrcaAgent Llama3.2 1B GGUF, Q4_K_M (0.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.5 GB.
VRAM requirement by quantization
Q2_K0.5 GB~75%Q3_K_M0.5 GB~83%Q4_K_M ★0.7 GB~89%Q5_K_M0.8 GB~92%Q6_K0.9 GB~95%Q8_01.1 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Isotonic.OrcaAgent Llama3.2 1B GGUF on a Mac?
Isotonic.OrcaAgent Llama3.2 1B GGUF requires at least 0.5 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 Isotonic.OrcaAgent Llama3.2 1B GGUF locally?
Yes — Isotonic.OrcaAgent Llama3.2 1B GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 0.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Isotonic.OrcaAgent Llama3.2 1B GGUF?
At Q4_K_M, Isotonic.OrcaAgent Llama3.2 1B GGUF can reach ~4417 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~993 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 ÷ 0.7 × 0.55 = ~4417 tok/s
Estimated speed at Q4_K_M (0.7 GB)
AMD Instinct MI300X~4417 tok/sNVIDIA GeForce RTX 4090~993 tok/sNVIDIA H100 SXM~3301 tok/sAMD Instinct MI250X~2731 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Isotonic.OrcaAgent Llama3.2 1B GGUF?
At Q4_K_M, the download is about 0.60 GB. The full-precision Q8_0 version is 1.00 GB. The smallest option (Q2_K) is 0.42 GB.
- Which GPUs can run Isotonic.OrcaAgent Llama3.2 1B GGUF?
35 consumer GPUs can run Isotonic.OrcaAgent Llama3.2 1B GGUF at Q4_K_M (0.7 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 Isotonic.OrcaAgent Llama3.2 1B GGUF?
33 devices with unified memory can run Isotonic.OrcaAgent Llama3.2 1B GGUF at Q4_K_M (0.7 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.