Aya 23 8B
Cohere · dense · 8B parameters · 8,192 context
Parameters
8B
Context Window
8K tokens
Architecture
Dense
Best GPU
A30
Cheapest API
$0.60/M
Intelligence Brief
Aya 23 8B is a 8B parameter DENSE model from Cohere, featuring Grouped Query Attention (GQA) with 32 layers and 4,096 hidden dimensions. With a 8,192 token context window, it supports multilingual. The most cost-effective API deployment is via cohere at $0.60/M output tokens. For self-hosted inference, A30 delivers optimal throughput at $332/month.
Architecture Details
Memory Requirements
BF16 Weights
16.0 GB
FP8 Weights
8.0 GB
INT4 Weights
4.0 GB
GPU Compatibility Matrix
Aya 23 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
cohere
$0.60/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 |
|---|---|---|---|
| cohere | $0.20 | $0.60 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| cohereBest Value | $0.20 | $0.60 | $4 |
Cost per 1,000 Requests
Short (500 tok)
$0.22
via cohere
Medium (2K tok)
$0.88
via cohere
Long (8K tok)
$2.80
via cohere
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 Aya 23 8B
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Aya 23 8B need for inference?
Aya 23 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 (~0.80 GB).
What is the best GPU for Aya 23 8B?
The top recommended GPU for Aya 23 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 Aya 23 8B inference cost?
Aya 23 8B API inference starts from $0.20/M input tokens and $0.60/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.