Llama 3.2 1B DeepSeek67B Distilled — Hardware Requirements & GPU Compatibility
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5 downloads
1 likesSpecifications
- Publisher
- enesarda22
- Family
- Llama 3
- Parameters
- 1B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-03-15
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How Much VRAM Does Llama 3.2 1B DeepSeek67B Distilled Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.8 GB | 5.0 GB | 0.42 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.8 GB | 5.0 GB | 0.44 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.8 GB | 5.1 GB | 0.49 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 1.0 GB | 5.2 GB | 0.60 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.1 GB | 5.3 GB | 0.71 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.2 GB | 5.4 GB | 0.82 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.4 GB | 5.6 GB | 1.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 3.2 1B DeepSeek67B Distilled?
Q4_K_M · 1.0 GBLlama 3.2 1B DeepSeek67B Distilled (Q4_K_M) requires 1.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 131K context window can add up to 4.2 GB, bringing total usage to 5.2 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Llama 3.2 1B DeepSeek67B Distilled?
Q4_K_M · 1.0 GB33 devices with unified memory can run Llama 3.2 1B DeepSeek67B Distilled, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Llama 3.2 1B DeepSeek67B Distilled need?
Llama 3.2 1B DeepSeek67B Distilled requires 1.0 GB of VRAM at Q4_K_M, or 1.4 GB at Q8_0. Full 131K context adds up to 4.2 GB (5.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1B × 4.8 bits ÷ 8 = 0.6 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.6 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M1.0 GBQ4_K_M + full context5.2 GB- What's the best quantization for Llama 3.2 1B DeepSeek67B Distilled?
For Llama 3.2 1B DeepSeek67B Distilled, Q4_K_M (1.0 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.1 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 0.8 GB.
VRAM requirement by quantization
IQ3_XS0.8 GB~73%Q3_K_S0.8 GB~77%IQ4_XS0.9 GB~87%Q4_K_M ★1.0 GB~89%Q5_K_S1.1 GB~92%Q8_01.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.2 1B DeepSeek67B Distilled on a Mac?
Llama 3.2 1B DeepSeek67B Distilled requires at least 0.8 GB at IQ3_XS, 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 Llama 3.2 1B DeepSeek67B Distilled locally?
Yes — Llama 3.2 1B DeepSeek67B Distilled can run locally on consumer hardware. At Q4_K_M quantization it needs 1.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.2 1B DeepSeek67B Distilled?
At Q4_K_M, Llama 3.2 1B DeepSeek67B Distilled can reach ~3005 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~676 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 ÷ 1.0 × 0.55 = ~3005 tok/s
Estimated speed at Q4_K_M (1.0 GB)
AMD Instinct MI300X~3005 tok/sNVIDIA GeForce RTX 4090~676 tok/sNVIDIA H100 SXM~2246 tok/sAMD Instinct MI250X~1858 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 3.2 1B DeepSeek67B Distilled?
At Q4_K_M, the download is about 0.60 GB. The full-precision Q8_0 version is 1.00 GB. The smallest option (IQ3_XS) is 0.41 GB.
- Which GPUs can run Llama 3.2 1B DeepSeek67B Distilled?
35 consumer GPUs can run Llama 3.2 1B DeepSeek67B Distilled at Q4_K_M (1.0 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 Llama 3.2 1B DeepSeek67B Distilled?
33 devices with unified memory can run Llama 3.2 1B DeepSeek67B Distilled at Q4_K_M (1.0 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.