Phi 1
Microsoft · dense · 1.3B parameters · 2,048 context
Parameters
1.3B
Context Window
2K tokens
Architecture
Dense
Best GPU
RTX 4070 Ti
Quality Score
38/100
Intelligence Brief
Phi 1 is a 1.3B parameter DENSE model from Microsoft, featuring Multi-Head Attention (MHA) with 24 layers and 2,048 hidden dimensions. With a 2,048 token context window, it supports code, math. On standardized benchmarks, it achieves MMLU 44, HumanEval 40, GSM8K 38. For self-hosted inference, RTX 4070 Ti delivers optimal throughput at $237/month.
Architecture Details
Memory Requirements
BF16 Weights
2.6 GB
FP8 Weights
1.3 GB
INT4 Weights
0.7 GB
GPU Compatibility Matrix
Phi 1 is compatible with 100% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
BF16 · 1 GPU · vllm
90/100
score
Throughput
994.4 tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$237
Cost/M Tokens
$0.09
BF16 · 1 GPU · vllm
90/100
score
Throughput
1.5K tok/s
Latency (ITL)
0.7ms
Est. TTFT
0ms
Cost/Month
$133
Cost/M Tokens
$0.03
BF16 · 1 GPU · vllm
90/100
score
Throughput
536.6 tok/s
Latency (ITL)
1.9ms
Est. TTFT
0ms
Cost/Month
$209
Cost/M Tokens
$0.15
Deployment Options
API Deployment
No API pricing available
Single GPU
RTX 4070 Ti
$237/mo
Min VRAM: 1 GB
Multi-GPU
RTX 4070 Ti
994.4 tok/s
Best available config
API Pricing Comparison
No API pricing data available for this model.
Performance Estimates
Throughput by GPU
VRAM Breakdown (RTX 4070 Ti, BF16)
Precision Impact
bf16
2.6 GB
weights/GPU
~994.4 tok/s
fp8
1.3 GB
weights/GPU
int4
0.7 GB
weights/GPU
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Phi 1
Self-Hosted Infrastructure
Similar Models
Phi 1.5
1.3B params · dense
Quality: 38
Phi 2
2.7B params · dense
Quality: 50
Phi 4 Mini
3.8B params · dense
Quality: 70
Eagle 2 1B
1.3B params · dense
Quality: 65
Llama 3.2 1B
1.24B params · dense
Quality: 38
from $0.03/M
Frequently Asked Questions
How much VRAM does Phi 1 need for inference?
Phi 1 requires approximately 2.6 GB of VRAM at BF16 precision, 1.3 GB at FP8, or 0.7 GB at INT4 quantization. Additional VRAM is needed for KV-cache (98304 bytes per token) and activations (~0.20 GB).
What is the best GPU for Phi 1?
The top recommended GPU for Phi 1 is the RTX 4070 Ti using BF16 precision. It achieves approximately 994.4 tokens/sec at an estimated cost of $237/month ($0.09/M tokens). Score: 90/100.
How much does Phi 1 inference cost?
Phi 1 inference costs vary by provider and GPU setup. Use our calculator for detailed cost estimates across all providers.