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Calculator › Phi-4 14B › RTX 4090 D

Phi-4 14B on RTX 4090 D (24GB) — VRAM, KV Cache & Throughput

Phi-4 14B (14.66B total, 14.66B active, Dense GQA) uses 8.28 GiB of 4-bit weights (Q4_K_M), requiring 10.4 GiB total at 8K context and 15.09 GiB at 32K on RTX 4090 D (~68.2 tok/s decode).

Interactive Sizing Workbench

Full Compatibility Report →
24 GB · 1008 GB/s
Fits with Safe Headroom
Measured GGUF shard size HF config.json ↗
Memory Allocation Breakdown (GiB) 10.4 GiB (11.17 GB) / 24 GiB (43%)
Model Weights: 8.28 GiB KV Cache & State: 1.56 GiB Runtime & Activation Buffers: 0.56 GiB System RAM Offload: 0 GiB
Required GPU Memory
10.4 GiB
11.17 GB (decimal)
Usable Hardware Memory
22.8 GiB
24 GB (physical)
Safe Headroom
+12.4 GiB
43% utilization
Est. Decode Throughput
~68.2 tok/s
14.66B active params
Est. Prefill Throughput
~2572 tok/s
4K Prompt TTFT: ~1593 ms
Est. Cost / 1M Tokens
$0.25
Dense GQA · MIT

Recommended Hardware for Current Configuration

Best Single GPU
RTX 4060 Ti 16GB (16GB)
~$430
Best Value / Used GPU
RTX 3060 12GB (12GB)
~$280
Apple Unified Memory
Ryzen AI Max+ 395 128GB
~$1,999
Cloud From
Vast.ai · $0.06 / hr
1x RTX 3060 12GB

Production Launch Commands

llama.cpp (GGUF) 
llama-server -hf unsloth/phi-4-GGUF:Q4_K_M \
  --ctx-size 8192 -ngl 999 --flash-attn
vLLM 
vllm serve microsoft/phi-4 \
  --max-model-len 8192 --gpu-memory-utilization 0.92
Live Token Speed Simulator (TTFT ~1593ms · ~68.2 tok/s)
[Phi-4 14B · 10.4 GiB] allocated on target GPU. Prefill completed in TTFT window; streaming autoregressive tokens at memory-bandwidth-bound rate...

Cloud GPU Comparison & Rent-vs-Buy TCO

Verified public rates across RunPod, Vast.ai, Vultr, and DigitalOcean.

Verified: 2026-11-04
Purchase break-even
~18.5 months
Est. local power: $3.51 / mo
Provider Recommended Instance Total VRAM Hourly Rate Monthly at Selected Hours Billing Mode Deploy
RunPod 1x RTX 3090 (Community Cloud) 24 GB $0.22 / hr $26.75 / mo Per-second Deploy GPU ↗
Vast.ai 1x RTX 3060 12GB (Verified Host) 12 GB $0.06 / hr $7.42 / mo Spot / On-demand Deploy GPU ↗
Vultr ($300 Free Credit*) 1x L40S 48GB (Dedicated Instance · $300 Credit*) 48 GB $1.67 / hr $203.19 / mo Hourly Deploy GPU ↗
Vultr Cloud GPU 1x L40S 48GB (Dedicated Instance) 48 GB $1.67 / hr $203.19 / mo Hourly Deploy GPU ↗
DigitalOcean 1x RTX 6000 Ada (Dedicated Instance) 48 GB $1.57 / hr $190.91 / mo Per-second Deploy GPU ↗

Rates reflect public listings on the verification date and fluctuate with regional inventory.

Fine-Tuning VRAM Requirements (QLoRA / LoRA / Full) — Phi-4 14B

QLoRA (4b) Fits with Safe Headroom
11.44 GiB
Model Weights: 8.28 GiB · Gradients + Optimizer: 2.05 GiB · Act: 1.11 GiB
Recommended GPU: RTX 4060 Ti 16GB (16GB)
LoRA (16b) Exceeds Available GPU Memory
32.02 GiB
Model Weights: 27.31 GiB · Gradients + Optimizer: 3.23 GiB · Act: 1.48 GiB
Recommended GPU: A40 48GB (48GB)
Full BF16 Exceeds Available GPU Memory
114.57 GiB
Model Weights: 27.31 GiB · Gradients + Optimizer: 84.66 GiB · Act: 2.6 GiB
Recommended GPU: Instinct MI300X 192GB (192GB)

Engineering Sizing Notes & Optimization Playbook

Quick Hardware Verdict

RTX 4090 D (24GB physical, 22.8 GiB usable) runs 4-bit (Q4_K_M) Phi-4 14B at 8K context using 10.4 GiB with 12.4 GiB of safe headroom (~68.2 tok/s single-stream decode).

KV Cache & Runtime Tuning

Phi-4 14B uses Dense GQA (14.66B total, 14.66B active per token). Moving from 8K to 32K context expands FP16 KV cache from 1.56 GiB to 6.25 GiB; enabling 8-bit KV cache (llama.cpp -ctk q8_0 -ctv q8_0 or vLLM --kv-cache-dtype fp8) saves 3.12 GiB at 32K (reducing total VRAM to 11.97 GiB).

VRAM Scaling Across Context Windows (4K to 128K, 4-bit Weights)

Phi-4 14B VRAM scaling from 4K to 128K context (4K: 9.62 GiB, 8K: 10.4 GiB, 32K: 15.09 GiB, 128K: 33.84 GiB) 4K 9.62 GiB (KV 0.78 GiB) 8K 10.4 GiB (KV 1.56 GiB) 32K 15.09 GiB (KV 6.25 GiB) 128K 33.84 GiB (KV 25 GiB) RTX 4090 D (24GB)
Model weights remain constant while attention KV cache grows with context length and batch size.

Quantization Precision & Memory Matrix — Phi-4 14B @ 8K

Quantization Format Weights (GiB) KV Cache Total VRAM (GiB) Decode Speed Compatibility (RTX 4090 D) Recommended GPU Data Source
Native Weights (BF16 / FP8 / MXFP4 — Lossless) (BF16) 27.31 GiB 1.56 GiB 29.43 GiB (31.6 GB) ~7.3 tok/s Exceeds Available GPU Memory Radeon AI PRO R9700 32GB (32GB) Official checkpoint size
FP8 / GGUF Q8_0 (8-bit — Near-Lossless) (Q8_0) 14.51 GiB 1.56 GiB 16.63 GiB (17.86 GB) ~40.6 tok/s Fits with Safe Headroom Arc Pro B60 24GB (24GB) Measured GGUF shard size
GGUF Q6_K (6-bit — High Precision) (Q6_K) 11.2 GiB 1.56 GiB 13.32 GiB (14.3 GB) ~51.7 tok/s Fits with Safe Headroom RTX 4060 Ti 16GB (16GB) Measured GGUF shard size
GGUF Q5_K_M (5-bit — Balanced) (Q5_K_M) 9.7 GiB 1.56 GiB 11.82 GiB (12.69 GB) ~59 tok/s Fits with Safe Headroom RTX 4060 Ti 16GB (16GB) Measured GGUF shard size
Q4_K_M / UD-Q4_K_XL / AWQ (4-bit — Recommended) (Q4_K_M) 8.28 GiB 1.56 GiB 10.4 GiB (11.17 GB) ~68.2 tok/s Fits with Safe Headroom RTX 4060 Ti 16GB (16GB) Measured GGUF shard size
GGUF IQ4_XS (4.25-bit — Compact) (IQ4_XS) 7.4 GiB 1.56 GiB 9.52 GiB (10.22 GB) ~75.4 tok/s Fits with Safe Headroom RTX 4060 Ti 16GB (16GB) Config-derived formula
GGUF Q3_K_M (3-bit — Low VRAM) (Q3_K_M) 6.7 GiB 1.56 GiB 8.82 GiB (9.47 GB) ~82.3 tok/s Fits with Safe Headroom RTX 4060 Ti 16GB (16GB) Measured GGUF shard size
GGUF UD-Q2_K_XL (2-bit — Extreme MoE) (UD-Q2_K_XL) 4.9 GiB 1.56 GiB 7.02 GiB (7.54 GB) ~107.9 tok/s Fits with Safe Headroom RTX 4060 Ti 16GB (16GB) Config-derived formula

Technical Specifications & FAQ

Does Phi-4 14B fit on RTX 4090 D (24GB)?

In 4-bit (Q4_K_M) at 8,192 context, Phi-4 14B requires 10.4 GiB total GPU memory (8.28 GiB weights + 1.56 GiB KV cache + 0.56 GiB runtime buffers) against 22.8 GiB usable on RTX 4090 D, delivering ~68.2 tok/s decode.

How much VRAM does Phi-4 14B need at 32K context?

At 32,768 context tokens, Phi-4 14B (Dense GQA) allocates 6.25 GiB for KV cache and state, bringing total 4-bit VRAM to 15.09 GiB (16.2 GB decimal).

Evaluate This Model on Other GPUs

RTX 3060 12GB (12GB)RTX 4060 Ti 16GB (16GB)RTX 4070 Ti Super (16GB)RTX 4080 Super (16GB)RTX 5070 12GB (12GB)RTX 5070 Ti (16GB)RTX 5080 (16GB)RTX 3090 (24GB)RTX 4090 (24GB)RTX 5090 (32GB)RTX 5090 D (32GB)RTX A6000 (48GB)RTX 6000 Ada (48GB)RTX PRO 6000 Blackwell (96GB)DGX Spark (GB10 128GB) (128GB)L4 24GB (24GB)A40 48GB (48GB)L20 48GB (48GB)L40S 48GB (48GB)A800 80GB SXM (80GB)A100 80GB SXM (80GB)H800 80GB SXM (80GB)H100 80GB SXM (80GB)H20 96GB HBM3 (96GB)H20 141GB HBM3e (141GB)H200 141GB SXM (141GB)B200 180GB (180GB)B300 288GB Blackwell Ultra (288GB)Ascend 910B 64GB (昇腾) (64GB)Ascend 910C 128GB (昇腾) (128GB)Atlas 300I Duo 96GB (310P) (96GB)MLU590 80GB (寒武纪思元) (80GB)DCU K100-AI 64GB (海光深算) (64GB)MetaX C500 64GB (沐曦曦云) (64GB)Kunlunxin P800 64GB (昆仑芯) (64GB)MTT S4000 48GB (摩尔线程) (48GB)Radeon RX 7900 XTX 24GB (24GB)Radeon AI PRO R9700 32GB (32GB)Ryzen AI Max+ 395 128GB (128GB)Instinct MI300X 192GB (192GB)Instinct MI325X 256GB (256GB)Instinct MI355X 288GB (288GB)Arc Pro B60 24GB (24GB)Gaudi 3 128GB HBM2e (128GB)Mac mini M4 Pro 64GB (64GB)MacBook Pro M4 Max 128GB (128GB)Mac Studio M4 Ultra 256GB (256GB)Mac Studio M3 Ultra 512GB (512GB)

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