Llama 3.2 11B Vision
Meta · dense · 11B parameters · 131,072 context
Parameters
11B
Context Window
128K tokens
Architecture
Dense
Best GPU
A100 40GB SXM
Cheapest API
$0.18/M
Intelligence Brief
Llama 3.2 11B Vision is a 11B parameter DENSE model from Meta, featuring Grouped Query Attention (GQA) with 40 layers and 4,096 hidden dimensions. With a 131,072 token context window, it supports tools, vision, structured output, code, math, multilingual. The most cost-effective API deployment is via together at $0.18/M output tokens. For self-hosted inference, A100 40GB SXM delivers optimal throughput at $807/month.
Architecture Details
Memory Requirements
BF16 Weights
22.0 GB
FP8 Weights
11.0 GB
INT4 Weights
5.5 GB
GPU Compatibility Matrix
Llama 3.2 11B Vision is compatible with 89% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
BF16 · 1 GPU · vllm
95/100
score
Throughput
381.7 tok/s
Latency (ITL)
2.6ms
Est. TTFT
0ms
Cost/Month
$807
Cost/M Tokens
$0.80
BF16 · 1 GPU · vllm
95/100
score
Throughput
439.8 tok/s
Latency (ITL)
2.3ms
Est. TTFT
0ms
Cost/Month
$845
Cost/M Tokens
$0.73
BF16 · 1 GPU · vllm
95/100
score
Throughput
381.7 tok/s
Latency (ITL)
2.6ms
Est. TTFT
0ms
Cost/Month
$655
Cost/M Tokens
$0.65
Deployment Options
API Deployment
together
$0.18/M
output tokens
Single GPU
A100 40GB SXM
$807/mo
Min VRAM: 11 GB
Multi-GPU
A4000 x2
178.0 tok/s
TP· $323/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| together | $0.18 | $0.18 | Cheapest |
| fireworks | $0.20 | $0.20 |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| togetherBest Value | $0.18 | $0.18 | $2 |
| fireworks | $0.20 | $0.20 | $2 |
Cost per 1,000 Requests
Short (500 tok)
$0.13
via together
Medium (2K tok)
$0.50
via together
Long (8K tok)
$1.80
via together
Performance Estimates
Throughput by GPU
VRAM Breakdown (A100 40GB SXM, BF16)
Precision Impact
bf16
22.0 GB
weights/GPU
~381.7 tok/s
fp8
11.0 GB
weights/GPU
int4
5.5 GB
weights/GPU
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Llama 3.2 11B Vision
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Frequently Asked Questions
How much VRAM does Llama 3.2 11B Vision need for inference?
Llama 3.2 11B Vision requires approximately 22.0 GB of VRAM at BF16 precision, 11.0 GB at FP8, or 5.5 GB at INT4 quantization. Additional VRAM is needed for KV-cache (163840 bytes per token) and activations (~1.00 GB).
What is the best GPU for Llama 3.2 11B Vision?
The top recommended GPU for Llama 3.2 11B Vision is the A100 40GB SXM using BF16 precision. It achieves approximately 381.7 tokens/sec at an estimated cost of $807/month ($0.80/M tokens). Score: 95/100.
How much does Llama 3.2 11B Vision inference cost?
Llama 3.2 11B Vision API inference starts from $0.18/M input tokens and $0.18/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.