Phi 4 Quantized.w8a8 — Hardware Requirements & GPU Compatibility
ChatMathCode
Phi 4 Quantized.w8a8 is a 14.7B-parameter open language model from RedHatAI in the Phi 4 family. It supports a context window of up to 16,384 tokens. At Q4_K_M it needs about 9.52 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (Q4_K_M · 8K)Best on 24 GB (Q4_K_M · ~69 tok/s · 16K)Macs from 16 GB (Q4_K_M · ~15 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Phi 4 Quantized.w8a8
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve RedHatAI/phi-4-quantized.w8a8 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3090 (24 GB) (11 GB headroom) · Q4_K_M
- Generation speed
- ~64 tok/s
- generation speed
- Cost per 1M output tokens
- $0.74
- per 1M output tokens
How Much VRAM Does Phi 4 Quantized.w8a8 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 7.0 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 16K context · ~94 tok/s | |
| 7.9 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~83 tok/s | |
| 9.5 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 16K context · ~69 tok/s | |
| 11.2 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 16K context · ~59 tok/s | |
| 12.8 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 16K context · ~51 tok/s | |
| 15.4 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 16K context · ~43 tok/s | |
| 30.0 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 33 GB |
Selected Q4_K_M
4-bit medium quantization12.5GBat 16K context
- Weights
- 8.8 GB
- KV cache
- 3.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 11.5 GB free · ~69 tok/s
≥50 tok/s needs ≥732 GB/s · RTX 5090 ~122, RTX 3090 Ti ~69
Which Hardware 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. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run Phi 4 Quantized.w8a8, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
16–18 GB
16–18 GB tier →fits · 3.5 GB free · up to 16K ctx
18 GPUs · 2 Mac classes · 2 device families
RTX 5080~66Simulate output on RTX 5080RTX 5070 Ti~61Simulate output on RTX 5070 TiRTX 4080 SUPER~50Simulate output on RTX 4080 SUPERM516 GB~11Simulate output on MacBook Air 13" M5 (16 GB)M3 Pro*18 GB~11Simulate output on MacBook Pro 14" M3 Pro (18 GB)iPad~11Simulate output on iPad Pro M5 13" (16 GB)Orin NX~7Simulate output on NVIDIA Jetson Orin NX 16GB20 GB
fits · 7.5 GB free · up to 16K ctx
1 GPU
RX 7900 XT~50Simulate output on RX 7900 XT32–40 GB
fits · 19.5 GB free · up to 16K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~122Simulate output on RTX 5090M5 Max36 GB~34Simulate output on Mac Studio M5 Max (36 GB)M532 GB~11Simulate output on MacBook Air 13" M5 (32 GB)Orin~14Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~9Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Phi 4 Quantized.w8a8
Phi 4 family · 7 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Apriel 1.6 15B ThinkerServiceNow-AI | — | 13.2 GB | ~67 | same size |
| Phi 3 Medium 128k InstructMicrosoft | — | 12 GB | ~72 | same size |
| Qwen1.5 14BAlibaba | — | 22.2 GB | ~63 | same size |
| JiRackUltra 14BCMSManhattan | — | 12.4 GB | ~69 | same size |
| Nemotron Terminal 14BNVIDIA | — | 11.9 GB | ~69 | same size |
| Ministral 3 14B Reasoning 2512Mistral AI | — | 12 GB | ~72 | same size |
Specifications
- Publisher
- RedHatAI
- Family
- Phi 4
- Parameters
- 14.7B
- Architecture
- Phi3ForCausalLM
- Context Length
- 16,384 tokens
- Vocabulary Size
- 100,352
- Release Date
- 2025-03-03
- License
- MIT
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 30.0 GB at BF16. 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 3.6 GB (at full 16K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Phi 4 Quantized.w8a8?
Yes, at Q8_0 (15.4 GB) or lower. Higher quantizations like BF16 (30.0 GB) exceed the NVIDIA GeForce RTX 4090's 24 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. Q5_K_M (11.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 7.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Phi 4 Quantized.w8a8 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Phi 4 Quantized.w8a8. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 9.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Phi 4 Quantized.w8a8 locally?
Yes — Phi 4 Quantized.w8a8 can run locally on consumer hardware. 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 ~504 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 9.52 × 0.65 = ~546 tok/s
Estimated speed at Q4_K_M (9.5 GB)
Real-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 BF16 version is 29.32 GB. The smallest option (Q2_K) is 6.23 GB.
Which GPUs can run Phi 4 Quantized.w8a8?
40 consumer GPUs can run Phi 4 Quantized.w8a8 at Q4_K_M. Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
Which devices can run Phi 4 Quantized.w8a8?
150 devices with unified memory can run Phi 4 Quantized.w8a8 at Q4_K_M (9.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.