Perplexity · Embeddings and rerank · 8.4B parameters · MIT
pplx-embed-v2-context-9b-preview
Needs 11 GB at FP8 / INT8. The smallest real configuration today is 1× RTX 3060 at Vast.ai, $0.04 per hour, about $26 a month at 730 hours.
Parameters8.4Bdense
Context256k tokensmaximum published
Checkpoint34 GBFP32, as published
LicenseMITcheck before commercial use
ReleasedSep 25, 2026documented date
Smallest configuration · FP8 / INT8
1× RTX 306012 GB VRAM · 84% used
- Per hour
- $0.04
- Per month
- $26
- Cheapest at
Vast.ai
What is documented about pplx-embed-v2-context-9b-preview
news checked 2026-10-27Contextual chunk embeddings for RAG (whole document encoded, one embedding per chunk); 2048-d with MRL 1024; native int8 unnormalized outputs. Explicitly a preview: embeddings may not be compatible with the final release. Date = HF repo creation.
- https://huggingface.co/perplexity-ai/pplx-embed-v2-context-9b-preview
- https://huggingface.co/api/models/perplexity-ai/pplx-embed-v2-context-9b-preview?blobs=true
How much VRAM it needs
Weights plus runtime overhead, then the KV cache that grows with the context you serve.
| Precision | Weights | Weights + headroom | 4k context | 32k context | 128k context | 256k context |
|---|---|---|---|---|---|---|
| FP32published checkpoint | 34 GB | 41 GB | 41 GB | 45 GB | 58 GB | 75 GB |
| FP8 / INT8estimated | 8 GB | 11 GB | 11 GB | 15 GB | 28 GB | 45 GB |
| INT4 / MXFP4estimated | 5 GB | 7 GB | 7 GB | 11 GB | 24 GB | 41 GB |
| KV cacheone sequence, FP16 cache, included above | — | — | 1 GB | 5 GB | 18 GB | 35 GB |
Cross-checked on Hugging Face Oct 28, 2026: 8.40B parameters in the safetensors headers, 33.6 GB of weight files, attention layout from config.json (32 layers, 4 KV heads × 256). Method: weights as published at the native precision, verified on Hugging Face 2026-10-28; other precisions estimated at 1 (8-bit) and 0.6 (4-bit) bytes per parameter; × 1.2 runtime headroom; KV cache = 4096 bytes per token per layer × 32 full-attention layers (FP16 cache, one sequence).config.json ↗
What it costs to run, GPU by GPU
The fewest GPUs of one type whose total memory fits the model at short context, priced from the cheapest eligible on-demand offer for exactly that count (45-day window, newest reading per provider), with the next providers beside it. Monthly = 730 hours.
| GPU | Smallest config | VRAM used | $ / hour | $ / month | Cheapest at |
|---|---|---|---|---|---|
| RTX 3060Best fit | 1× 12 GB12 GB total | 84% | $0.04 | $26 | |
| RTX A2000 | 1× 12 GB12 GB total | 84% | $0.04 | $26 | |
| T4 | 1× 16 GB16 GB total | 63% | $0.04 | $29 | |
| Tesla P40 | 1× 24 GB24 GB total | 42% | $0.04 | $30 | |
| GTX Titan X | 1× 12 GB12 GB total | 84% | $0.04 | $31 | |
| Titan Xp | 1× 12 GB12 GB total | 84% | $0.04 | $31 | |
| Tesla P100 | 1× 16 GB16 GB total | 63% | $0.04 | $33 | |
| Tesla V100 16GB | 1× 16 GB16 GB total | 63% | $0.06 | $46 | |
| GTX 1080 Ti | 1× 11 GB11 GB total | 92% tight | $0.06 | $47 | |
| RTX 2080 Ti | 1× 11 GB11 GB total | 92% tight | $0.07 | $49 | |
| Tesla V100 32GB | 1× 32 GB32 GB total | 32% | $0.07 | $50 | |
| RTX PRO 2000 | 1× 16 GB16 GB total | 63% | $0.07 | $51 | |
| RTX A4000 | 1× 16 GB16 GB total | 63% | $0.07 | $53 | |
| Tesla M40 | 1× 24 GB24 GB total | 42% | $0.07 | $54 | |
| RTX 3060 Ti | 2× 8 GB16 GB total | 63% | $0.08 | $61 | |
| RTX 5060 Ti | 1× 16 GB16 GB total | 63% | $0.08 | $61 | |
| RTX 4070 | 1× 12 GB12 GB total | 84% | $0.08 | $61 | |
| GTX 1660 | 2× 6 GB12 GB total | 84% | $0.09 | $64 | |
| RTX 3090 | 1× 24 GB24 GB total | 42% | $0.09 | $66 | |
| RTX 4070 Super | 1× 12 GB12 GB total | 84% | $0.10 | $70 | |
| GTX 1070 | 2× 8 GB16 GB total | 63% | $0.10 | $70 | |
| GTX 1080 | 2× 8 GB16 GB total | 63% | $0.10 | $76 | |
| RTX 3080 Ti | 1× 12 GB12 GB total | 84% | $0.10 | $76 | |
| L4 | 1× 24 GB24 GB total | 42% | $0.10 | $76 | |
| The 24 cheapest of 99 GPU types that fit. Every GPU, any precision and context, in the finder | |||||
