Phi 3 Small 8k Instruct — Hardware Requirements & GPU Compatibility
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Phi 3 Small 8k Instruct is a 7.4B-parameter open language model from Microsoft in the Phi 3 family. It supports a context window of up to 8,192 tokens. At BF16 it needs about 15.35 GB of VRAM — see which GPUs and Macs can run it below.
13.0K downloads
178 likesSpecifications
- Publisher
- Microsoft
- Family
- Phi 3
- Parameters
- 7.4B
- Architecture
- Phi3SmallForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 100,352
- Release Date
- 2024-05-07
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Phi 3 Small 8k Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 15.3 GB | 16.2 GB | 14.78 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Phi 3 Small 8k Instruct?
BF16 · 15.3 GBPhi 3 Small 8k Instruct (BF16) requires 15.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. Using the full 8K context window can add up to 0.8 GB, bringing total usage to 16.2 GB. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Phi 3 Small 8k Instruct?
BF16 · 15.3 GB47 devices with unified memory can run Phi 3 Small 8k Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Phi 3 Small 8k Instruct need?
Phi 3 Small 8k Instruct requires 15.3 GB of VRAM at BF16. Full 8K context adds up to 0.8 GB (16.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.4B × 16 bits ÷ 8 = 14.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.4 GB (at full 8K context)
VRAM usage by quantization
BF1615.3 GBBF16 + full context16.2 GB- Can I run Phi 3 Small 8k Instruct on a Mac?
Phi 3 Small 8k Instruct requires at least 15.3 GB at BF16, 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 Phi 3 Small 8k Instruct locally?
Yes — Phi 3 Small 8k Instruct can run locally on consumer hardware. At BF16 quantization it needs 15.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Phi 3 Small 8k Instruct?
At BF16, Phi 3 Small 8k Instruct can reach ~287 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~43 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 15.3 × 0.65 = ~339 tok/s
Estimated speed at BF16 (15.3 GB)
~339 tok/s~43 tok/s~339 tok/s~287 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Phi 3 Small 8k Instruct?
At BF16, the download is about 14.78 GB.
- Which GPUs can run Phi 3 Small 8k Instruct?
26 consumer GPUs can run Phi 3 Small 8k Instruct at BF16 (15.3 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Phi 3 Small 8k Instruct?
49 devices with unified memory can run Phi 3 Small 8k Instruct at BF16 (15.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.