Aya 23 35B
Cohere · dense · 35B parameters · 131,072 context
Parameters
35B
Context Window
128K tokens
Architecture
Dense
Best GPU
H20
Cheapest API
$1.50/M
Intelligence Brief
Aya 23 35B is a 35B parameter DENSE model from Cohere, featuring Grouped Query Attention (GQA) with 32 layers and 8,192 hidden dimensions. With a 131,072 token context window, it supports multilingual. The most cost-effective API deployment is via cohere at $1.50/M output tokens. For self-hosted inference, H20 delivers optimal throughput at $940/month.
Architecture Details
Memory Requirements
BF16 Weights
70.0 GB
FP8 Weights
35.0 GB
INT4 Weights
17.5 GB
GPU Compatibility Matrix
Aya 23 35B is compatible with 57% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
956.1 tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$940
Cost/M Tokens
$0.37
FP8 · 1 GPU · tensorrt-llm
98/100
score
Throughput
1.1K tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$4261
Cost/M Tokens
$1.54
FP8 · 1 GPU · tensorrt-llm
95/100
score
Throughput
1.1K tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$2553
Cost/M Tokens
$0.93
Deployment Options
API Deployment
cohere
$1.50/M
output tokens
Single GPU
H20
$940/mo
Min VRAM: 35 GB
Multi-GPU
RTX A6000 x2
104.7 tok/s
TP· $930/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| cohere | $0.50 | $1.50 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| cohereBest Value | $0.50 | $1.50 | $10 |
Cost per 1,000 Requests
Short (500 tok)
$0.55
via cohere
Medium (2K tok)
$2.20
via cohere
Long (8K tok)
$7.00
via cohere
Performance Estimates
Throughput by GPU
VRAM Breakdown (H20, FP8)
Precision Impact
bf16
70.0 GB
weights/GPU
fp8
35.0 GB
weights/GPU
~956.1 tok/s
int4
17.5 GB
weights/GPU
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Aya 23 35B
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Aya 23 35B need for inference?
Aya 23 35B requires approximately 70.0 GB of VRAM at BF16 precision, 35.0 GB at FP8, or 17.5 GB at INT4 quantization. Additional VRAM is needed for KV-cache (131072 bytes per token) and activations (~2.00 GB).
What is the best GPU for Aya 23 35B?
The top recommended GPU for Aya 23 35B is the H20 using FP8 precision. It achieves approximately 956.1 tokens/sec at an estimated cost of $940/month ($0.37/M tokens). Score: 100/100.
How much does Aya 23 35B inference cost?
Aya 23 35B API inference starts from $0.50/M input tokens and $1.50/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.