Llama32 3B En Emo 2000 Stp — Hardware Requirements & GPU Compatibility
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Llama32 3B En Emo 2000 Stp is a 3.3B-parameter open language model from iFaz in the Llama 3 family. It supports a context window of up to 131,072 tokens. At FP16 it needs about 7.14 GB of VRAM — see which GPUs and Macs can run it below.
15 downloads
1 likesSpecifications
- Publisher
- iFaz
- Family
- Llama 3
- Parameters
- 3.3B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-03-07
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Llama32 3B En Emo 2000 Stp Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 7.1 GB | 21.9 GB | 6.60 GB | Full half-precision — baseline for inference |
Which GPUs Can Run Llama32 3B En Emo 2000 Stp?
FP16 · 7.1 GBLlama32 3B En Emo 2000 Stp (FP16) requires 7.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 10+ GB is recommended. Using the full 131K context window can add up to 14.8 GB, bringing total usage to 21.9 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080.
Runs great
— Plenty of headroom
Which Devices Can Run Llama32 3B En Emo 2000 Stp?
FP16 · 7.1 GB33 devices with unified memory can run Llama32 3B En Emo 2000 Stp, 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 Llama32 3B En Emo 2000 Stp need?
Llama32 3B En Emo 2000 Stp requires 7.1 GB of VRAM at FP16. Full 131K context adds up to 14.8 GB (21.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.3B × 16 bits ÷ 8 = 6.6 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 15.3 GB (at full 131K context)
VRAM usage by quantization
FP167.1 GBFP16 + full context21.9 GB- Can I run Llama32 3B En Emo 2000 Stp on a Mac?
Llama32 3B En Emo 2000 Stp requires at least 7.1 GB at FP16, 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 Llama32 3B En Emo 2000 Stp locally?
Yes — Llama32 3B En Emo 2000 Stp can run locally on consumer hardware. At FP16 quantization it needs 7.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama32 3B En Emo 2000 Stp?
At FP16, Llama32 3B En Emo 2000 Stp can reach ~408 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~92 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 ÷ 7.1 × 0.55 = ~408 tok/s
Estimated speed at FP16 (7.1 GB)
~408 tok/s~92 tok/s~305 tok/s~252 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama32 3B En Emo 2000 Stp?
At FP16, the download is about 6.60 GB.
- Which GPUs can run Llama32 3B En Emo 2000 Stp?
35 consumer GPUs can run Llama32 3B En Emo 2000 Stp at FP16 (7.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 27 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llama32 3B En Emo 2000 Stp?
33 devices with unified memory can run Llama32 3B En Emo 2000 Stp at FP16 (7.1 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.