LLMLlama

meta-llama/Llama-3.2-1B-Instruct

meta-llama/Llama-3.2-1B-Instruct

Parameters

1B

Context

Downloads

9.2M

Likes

1.6K

Architecture

View on HuggingFace

GPU Requirements

VRAM / RAM / disk estimates by quantization and usage scenario

QuantizationMinimumRecommendedProduction
FP1616bit
7.3 GB
RAM 32.0 · Disk 41.9
23.2 GB
RAM 34.8 · Disk 67.9
120.2 GB
RAM 180.3 · Disk 106.9
FP88bit
6.1 GB
RAM 32.0 · Disk 40.5
21.9 GB
RAM 32.9 · Disk 66.5
118.9 GB
RAM 178.3 · Disk 105.5
INT88bit
6.1 GB
RAM 32.0 · Disk 40.5
21.9 GB
RAM 32.9 · Disk 66.5
118.9 GB
RAM 178.3 · Disk 105.5
AWQ4bit
5.6 GB
RAM 32.0 · Disk 39.8
21.3 GB
RAM 32.0 · Disk 65.8
118.3 GB
RAM 177.4 · Disk 104.8
GPTQ4bit
5.6 GB
RAM 32.0 · Disk 39.8
21.4 GB
RAM 32.0 · Disk 65.8
118.3 GB
RAM 177.4 · Disk 104.8
GGUF4bit
5.6 GB
RAM 32.0 · Disk 39.8
21.4 GB
RAM 32.0 · Disk 65.8
118.3 GB
RAM 177.4 · Disk 104.8

GPU Recommendations

Best ValueBEST

1× RTX 5090

32 GB VRAM

$257/mo

≈ 3733.3 tok/s

Effective production throughput ≈ 2613.3 tok/s

score 0.6753

Deploy on vast ↗
Cheapest

1× RTX 3090

24 GB VRAM

$76.5/mo

≈ 1950 tok/s

Effective production throughput ≈ 1365 tok/s

score 0.658

Deploy on vast ↗
Performance

8× H200 SXM

1128 GB VRAM

$20,440/mo

≈ 32000 tok/s

Effective production throughput ≈ 22400 tok/s

score 0.2197

Min Complexity

1× RTX 4090

24 GB VRAM

$99.0/mo

≈ 2100 tok/s

Effective production throughput ≈ 1470 tok/s

score 0.658

Deploy on vast ↗

💬 Community — real deployment experience

0 Articles · 0 Benchmarks

View Community →

GPUs that fit (single card)

RTX 3090

24 GB VRAM

$76.5

/mo · vast

RTX 4090

24 GB VRAM

$99.0

/mo · vast

RTX 4090D

24 GB VRAM

$117

/mo · vast

L4

24 GB VRAM

$147

/mo · vast

A10

24 GB VRAM

$176

/mo · vast

AmciHub

AI Model Deployment & Compute Intelligence — analyze AI model requirements, GPU performance, cloud pricing and deployment costs to find the right deployment solution.

© 2026 AmciHub. All rights reserved. Model → Requirement → GPU → Benchmark → Cloud → Cost → Recommendation