Llama 3.3 8B
Meta · dense · 8B parameters · 131,072 context
Parameters
8B
Context Window
128K tokens
Architecture
Dense
Best GPU
A30
Cheapest API
$0.18/M
Intelligence Brief
Llama 3.3 8B is a 8B parameter DENSE model from Meta, featuring Grouped Query Attention (GQA) with 32 layers and 4,096 hidden dimensions. With a 131,072 token context window, it supports tools, structured output, code, math, multilingual. The most cost-effective API deployment is via together at $0.18/M output tokens. For self-hosted inference, A30 delivers optimal throughput at $332/month.
Provider pricing
1 provider · canonical: together| Provider | Input $/M | Output $/M ▲ | Notes |
|---|---|---|---|
| togethercanonical | $0.180 | $0.180 | cheapest input · cheapest output |
Prices update via the nightly pricing cron + admin approvals at /admin/ingest-queue. The leaderboard's Input/Output cells show the canonical rate above; this table shows the full spread.
Recent changes
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Picks: same family first, then same vendor within ±2× params, then top tag-overlap matches. Price shown is the cheapest Output $/M across providers — the row's page shows the canonical anchor.
Architecture Details
Memory Requirements
BF16 Weights
16.0 GB
FP8 Weights
8.0 GB
INT4 Weights
4.0 GB
GPU Compatibility Matrix
Llama 3.3 8B is compatible with 90% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
BF16 · 1 GPU · vllm
100/100
score
Throughput
314.9 tok/s
Latency (ITL)
3.2ms
Est. TTFT
1ms
Cost/Month
$332
Cost/M Tokens
$0.40
BF16 · 1 GPU · vllm
100/100
score
Throughput
340.2 tok/s
Latency (ITL)
2.9ms
Est. TTFT
1ms
Cost/Month
$370
Cost/M Tokens
$0.41
BF16 · 1 GPU · vllm
100/100
score
Throughput
315.9 tok/s
Latency (ITL)
3.2ms
Est. TTFT
1ms
Cost/Month
$180
Cost/M Tokens
$0.22
Deployment Options
API Deployment
together
$0.18/M
output tokens
Single GPU
A30
$332/mo
Min VRAM: 8 GB
Multi-GPU
RTX 3060 x2
190.4 tok/s
TP· $114/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| together | $0.18 | $0.18 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| togetherBest Value | $0.18 | $0.18 | $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 (A30, BF16)
Precision Impact
bf16
16.0 GB
weights/GPU
~314.9 tok/s
fp8
8.0 GB
weights/GPU
int4
4.0 GB
weights/GPU
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Llama 3.3 8B
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Llama 3.3 8B need for inference?
Llama 3.3 8B requires approximately 16.0 GB of VRAM at BF16 precision, 8.0 GB at FP8, or 4.0 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 Llama 3.3 8B?
The top recommended GPU for Llama 3.3 8B is the A30 using BF16 precision. It achieves approximately 314.9 tokens/sec at an estimated cost of $332/month ($0.40/M tokens). Score: 100/100.
How much does Llama 3.3 8B inference cost?
Llama 3.3 8B 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.