Phi 4 Quantized.w8a8 — Hardware Requirements & GPU Compatibility
ChatMathCode
438 downloads
3 likesSpecifications
- Publisher
- RedHatAI
- Family
- Phi 4
- Parameters
- 14.7B
- Architecture
- Phi3ForCausalLM
- Context Length
- 16,384 tokens
- Vocabulary Size
- 100,352
- Release Date
- 2025-09-25
- License
- MIT
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HuggingFace
How Much VRAM Does Phi 4 Quantized.w8a8 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 7.0 GB | 9.9 GB | 6.23 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 7.1 GB | 10.1 GB | 6.41 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 7.9 GB | 10.8 GB | 7.15 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 8.1 GB | 11.0 GB | 7.33 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 9.5 GB | 12.4 GB | 8.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 11.2 GB | 14.1 GB | 10.45 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 12.8 GB | 15.8 GB | 12.10 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 15.4 GB | 18.3 GB | 14.66 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Phi 4 Quantized.w8a8?
Q4_K_M · 9.5 GBPhi 4 Quantized.w8a8 (Q4_K_M) requires 9.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. Using the full 16K context window can add up to 2.9 GB, bringing total usage to 12.4 GB. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Phi 4 Quantized.w8a8?
Q4_K_M · 9.5 GB27 devices with unified memory can run Phi 4 Quantized.w8a8, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Phi 4 Quantized.w8a8 need?
Phi 4 Quantized.w8a8 requires 9.5 GB of VRAM at Q4_K_M, or 15.4 GB at Q8_0. Full 16K context adds up to 2.9 GB (12.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.7B × 4.8 bits ÷ 8 = 8.8 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 3.6 GB (at full 16K context)
VRAM usage by quantization
Q4_K_M9.5 GBQ4_K_M + full context12.4 GB- What's the best quantization for Phi 4 Quantized.w8a8?
For Phi 4 Quantized.w8a8, Q4_K_M (9.5 GB) offers the best balance of quality and VRAM usage. Q4_K_L (9.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 5.1 GB.
VRAM requirement by quantization
IQ2_XS5.1 GB~57%Q3_K_S7.1 GB~77%IQ4_XS8.6 GB~87%Q4_K_M ★9.5 GB~89%Q4_K_L9.7 GB~90%Q8_015.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Phi 4 Quantized.w8a8 on a Mac?
Phi 4 Quantized.w8a8 requires at least 5.1 GB at IQ2_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 Phi 4 Quantized.w8a8 locally?
Yes — Phi 4 Quantized.w8a8 can run locally on consumer hardware. At Q4_K_M quantization it needs 9.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Phi 4 Quantized.w8a8?
At Q4_K_M, Phi 4 Quantized.w8a8 can reach ~306 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~69 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 ÷ 9.5 × 0.55 = ~306 tok/s
Estimated speed at Q4_K_M (9.5 GB)
AMD Instinct MI300X~306 tok/sNVIDIA GeForce RTX 4090~69 tok/sNVIDIA H100 SXM~229 tok/sAMD Instinct MI250X~189 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Phi 4 Quantized.w8a8?
At Q4_K_M, the download is about 8.80 GB. The full-precision Q8_0 version is 14.66 GB. The smallest option (IQ2_XS) is 4.40 GB.
- Which GPUs can run Phi 4 Quantized.w8a8?
28 consumer GPUs can run Phi 4 Quantized.w8a8 at Q4_K_M (9.5 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Phi 4 Quantized.w8a8?
27 devices with unified memory can run Phi 4 Quantized.w8a8 at Q4_K_M (9.5 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.