HARDWARE FIT CHECK • Microsoft Phi-4 14B + AMD Radeon RX 7900 XT (20GB GDDR6)13.24 GB / 20 GB VRAM

Can I Run Microsoft Phi-4 14B on AMD Radeon RX 7900 XT (20GB GDDR6)?

Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Microsoft Phi-4 14B (14.7B) on AMD Radeon RX 7900 XT (20GB GDDR6) (20 GB).

✓ FITS IN VRAM13.24 GB (66%)~48 t/s

Target Hardware & Model

interactive
High-accuracy mathematical and scientific reasoning on 16GB GPUs
Microsoft Phi-4 14B on AMD Radeon RX 7900 XT (20GB GDDR6)

✓ Fits Comfortably in VRAM

✓ FITS IN VRAM
Required VRAM
13.24 GB
66% of hardware capacity (20 GB)
Est. Decode Speed
~48 tok/s
Dense model @ 800 GB/s
Available Headroom
+6.76 GB
Safe for OS display & full context
Memory Allocation & Headroom
13.24 GB allocated of 20 GB capacity
66% utilized
Weights: 8.82 GB
KV: (32K): 3.36 GB
Overhead: 1.06 GB
Free Headroom: +6.76 GB
Architecture & Throughput Transparency: Single-user decode speed (~48 tok/s) is memory-bandwidth bound at batch size = 1 on AMD Radeon RX 7900 XT (20GB GDDR6) (800 GB/s). Prompt prefill / TTFT (~696 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).

Local Hardware Configurations & Pricing

High-bandwidth VRAM specs and current market prices
Consumer GPU
32 GB VRAM

NVIDIA GeForce RTX 5090 (32GB GDDR7)

Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization

1792 GB/s575W TDP
Consumer GPU
24 GB VRAM

NVIDIA GeForce RTX 4090 (24GB GDDR6X)

The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models

1008 GB/s450W TDP
Multi-GPU Rig
48 GB VRAM

2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)

Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0

1648 GB/s700W TDP
Consumer GPU
20 GB VRAM

AMD Radeon RX 7900 XT (20GB GDDR6)

Sub-$700 GPU with 20GB VRAM fitting 14B Q8 and 27B Q4 models

800 GB/s315W TDP

Quantization Precision Matrix

32K ctx • KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw29.4 GB3.36 GB34.54 GB (173%)0 t/sOOM
Q8_08.5 bpw15.58 GB3.36 GB20.24 GB (101%)~6 t/sPARTIAL
Q6_K6.56 bpw12.05 GB3.36 GB16.58 GB (83%)~36 t/sFITS
Q5_K_M5.68 bpw10.44 GB3.36 GB14.92 GB (75%)~41 t/sFITS
Q4_K_M4.8 bpw8.82 GB3.36 GB13.24 GB (66%)~48 t/sFITS
FP44.25 bpw7.79 GB3.36 GB12.17 GB (61%)~54 t/sFITS
IQ3_XS3.3 bpw6.03 GB3.36 GB10.35 GB (52%)~68 t/sFITS

Export Full Architecture & Infrastructure Spec

Download an instant Markdown architecture spec with exact memory math, hardware benchmarks, and runtime CLI flags.

FAQ & Reference

Frequently Asked Questions: Microsoft Phi-4 14B on AMD Radeon RX 7900 XT (20GB GDDR6)

How much VRAM does Microsoft Phi-4 14B need on AMD Radeon RX 7900 XT (20GB GDDR6)?

At standard 4-bit quantization (Q4_K_M) with 32K context, Microsoft Phi-4 14B requires 13.24 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will Microsoft Phi-4 14B achieve on AMD Radeon RX 7900 XT (20GB GDDR6)?

Based on AMD Radeon RX 7900 XT (20GB GDDR6)'s 800 GB/s memory bandwidth, expected decode speed is approximately 48 tokens per second.

Can I expand context beyond 32K tokens?

Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.