HARDWARE FIT CHECK โข Microsoft Phi-4 Mini 3.8B + NVIDIA RTX 4070 Ti Super (16GB)3.92 GB / 16 GB VRAM
Can I Run Microsoft Phi-4 Mini 3.8B on NVIDIA RTX 4070 Ti Super (16GB)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Microsoft Phi-4 Mini 3.8B (3.8B) on NVIDIA RTX 4070 Ti Super (16GB) (16 GB).
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Runs 100% in GPU VRAM (Optimal Headroom)3.92 GB (25%)~181 t/s
Microsoft Phi-4 Mini 3.8B on NVIDIA RTX 4070 Ti Super (16GB)
โ Fits Comfortably in VRAM
โ FITS IN VRAM
Required VRAM
3.92 GB
25% of hardware capacity (16 GB)Est. Decode Speed
~181 tok/s
Dense model @ 672 GB/sAvailable Headroom
+12.08 GB
Safe for OS display & full contextMemory Allocation & Headroom
3.92 GB allocated of 16 GB capacity
25% utilized
Weights: 2.28 GB
KV: (32K): 0.81 GB
Overhead: 0.83 GB
Free Headroom: +12.08 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~181 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA RTX 4070 Ti Super (16GB) (672 GB/s). Prompt prefill / TTFT (~2,625 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run phi4-mini:3.8bCloud Instance Options for Microsoft Phi-4 Mini 3.8B
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) โ $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) โ $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)Quantization Precision Matrix
32K ctx โข KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 7.6 GB | 0.81 GB | 9.43 GB (59%) | ~58 t/s | FITS |
| Q8_08.5 bpw | 4.03 GB | 0.81 GB | 5.73 GB (36%) | ~106 t/s | FITS |
| Q6_K6.56 bpw | 3.12 GB | 0.81 GB | 4.79 GB (30%) | ~136 t/s | FITS |
| Q5_K_M5.68 bpw | 2.7 GB | 0.81 GB | 4.35 GB (27%) | ~155 t/s | FITS |
| Q4_K_M4.8 bpw | 2.28 GB | 0.81 GB | 3.92 GB (25%) | ~181 t/s | FITS |
| FP44.25 bpw | 2.01 GB | 0.81 GB | 3.64 GB (23%) | ~203 t/s | FITS |
| IQ3_XS3.3 bpw | 1.56 GB | 0.81 GB | 3.17 GB (20%) | ~255 t/s | FITS |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesConsumer GPU16 GB VRAM
NVIDIA RTX 4070 Ti Super (16GB)
Mid-range 16GB gaming + local AI daily driver
672 GB/s285W TDP
$799Amazon Prime โ
Consumer GPU32 GB VRAM
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27Bโ35B models & Blackwell FP4 quantization
1792 GB/s575W TDP
$1,999Amazon Prime โ
Consumer GPU24 GB VRAM
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14Bโ32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon Prime โ
Multi-GPU Rig48 GB VRAM
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
936 GB/s700W TDP
$1,550Amazon Prime โ
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