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Updated minutes ago
HuggingFace

Zephyr 7B

Hugging Face · dense · 7B parameters · 32,768 context

Quality
50.0

Parameters

7B

Context Window

32K tokens

Architecture

Dense

Best GPU

A10G

Intelligence Brief

Zephyr 7B is a 7B parameter DENSE model from Hugging Face, featuring Grouped Query Attention (GQA) with 32 layers and 4,096 hidden dimensions. With a 32,768 token context window, it supports code, math. For self-hosted inference, A10G delivers optimal throughput at $285/month.

Architecture Details

TypeDENSE
Total Parameters7B
Active Parameters7B
Layers32
Hidden Dimension4,096
Attention Heads32
KV Heads8
Head Dimension128
Vocab Size32,000

Memory Requirements

BF16 Weights

14.0 GB

FP8 Weights

7.0 GB

INT4 Weights

3.5 GB

KV-Cache per Token131072 bytes
Activation Estimate1.00 GB

GPU Compatibility Matrix

Zephyr 7B is compatible with 95% of GPU configurations across 41 GPUs at 3 precision levels.

BF16 (Full)
FP8 (Half)
INT4 (Quarter)
Blackwell(7 GPUs)
B200 NVL (pair)360GB
B300288GB
B100 SXM192GB
GB200 NVL72 (per GPU)192GB
Hopper(7 GPUs)
H100 NVL 94GB (per GPU pair)188GB
H200 SXM141GB
H2096GB
GH20096GB
Ada Lovelace(11 GPUs)
L40S48GB
L4048GB
RTX 6000 Ada48GB
L2048GB
Ampere(16 GPUs)
A100 80GB SXM80GB
A100 80GB PCIe80GB
A1664GB
RTX A600048GB
Legend:No fitVery tightTightModerateGoodExcellent

GPU Recommendations

A10Goptimal

BF16 · 1 GPU · vllm

100/100

score

Throughput

231.4 tok/s

Latency (ITL)

4.3ms

Est. TTFT

1ms

Cost/Month

$285

Cost/M Tokens

$0.47

Use this config →
A30optimal

BF16 · 1 GPU · vllm

100/100

score

Throughput

359.9 tok/s

Latency (ITL)

2.8ms

Est. TTFT

0ms

Cost/Month

$332

Cost/M Tokens

$0.35

Use this config →
RTX 4090optimal

BF16 · 1 GPU · vllm

100/100

score

Throughput

388.8 tok/s

Latency (ITL)

2.6ms

Est. TTFT

0ms

Cost/Month

$370

Cost/M Tokens

$0.36

Use this config →

Deployment Options

API

API Deployment

No API pricing available

Self-Hosted

Single GPU

A10G

$285/mo

Min VRAM: 7 GB

Scale

Multi-GPU

RTX 3080 x2

452.6 tok/s

TP· $266/mo

API Pricing Comparison

No API pricing data available for this model.

Performance Estimates

Throughput by GPU

A10G
231.4 tok/s
A30
359.9 tok/s
RTX 4090
388.8 tok/s

VRAM Breakdown (A10G, BF16)

Weights
Act
Weights 14.0 GBKV-Cache 2.1 GBActivations 8.0 GBOverhead 1.1 GB

Precision Impact

bf16

14.0 GB

weights/GPU

~231.4 tok/s

fp8

7.0 GB

weights/GPU

int4

3.5 GB

weights/GPU

Capabilities

Features

Tool Use Vision Code Math Reasoning Multilingual Structured Output

Supported Frameworks

vllmsglangtgitensorrt-llmollama

Supported Precisions

BF16 (default)FP8INT4

Where to Deploy Zephyr 7B

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Frequently Asked Questions

How much VRAM does Zephyr 7B need for inference?

Zephyr 7B requires approximately 14.0 GB of VRAM at BF16 precision, 7.0 GB at FP8, or 3.5 GB at INT4 quantization. Additional VRAM is needed for KV-cache (131072 bytes per token) and activations (~1.00 GB).

What is the best GPU for Zephyr 7B?

The top recommended GPU for Zephyr 7B is the A10G using BF16 precision. It achieves approximately 231.4 tokens/sec at an estimated cost of $285/month ($0.47/M tokens). Score: 100/100.

How much does Zephyr 7B inference cost?

Zephyr 7B inference costs vary by provider and GPU setup. Use our calculator for detailed cost estimates across all providers.