Aleph Alpha · Chat and reasoning · 78.1B parameters · 3.46B active · Apache-2.0
Kolibri-1
Needs 95 GB at FP8 / INT8. The smallest real configuration today is 4× RTX 3090 at Clore.ai, $0.25 per hour, about $183 a month at 730 hours.
Parameters78.1B3.46B active per token
Context256k tokensmaximum published
Checkpoint79 GBFP8, as published
LicenseApache-2.0check before commercial use
ReleasedOct 3, 2026documented date
Smallest configuration · FP8 / INT8
4× RTX 309096 GB VRAM · 99% used
- Per hour
- $0.25
- Per month
- $183
- Cheapest at
Clore.ai
What is documented about Kolibri-1
news checked 2026-10-27Card: 'Release Date 3rd of October 2026', 78B total / 3.46B active. Native context 262,144; validated up to 1,048,576 but the card recommends at most 262,144. 384 experts (1 shared + 6 routed). 4:1 SWA:GQA. Bilingual English/German, with about 62.5% EN and 23.9% DE in pretraining.
- https://huggingface.co/Aleph-Alpha/Kolibri-1
- https://huggingface.co/Aleph-Alpha/Kolibri-1/raw/main/config.json
- https://huggingface.co/api/models/Aleph-Alpha/Kolibri-1?blobs=true
Memory · 2 precisions · 5 context lengthsHow 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 |
|---|---|---|---|---|---|---|
| FP8published checkpoint | 79 GB | 95 GB | 95 GB | 96 GB | 98 GB | 100 GB |
| INT4 / MXFP4estimated | 47 GB | 57 GB | 57 GB | 57 GB | 59 GB | 62 GB |
| KV cacheone sequence, FP16 cache, included above | — | — | 1 GB | 1 GB | 3 GB | 6 GB |
Cross-checked on Hugging Face Oct 28, 2026: 78.1B parameters in the safetensors headers, 78.8 GB of weight files, attention layout from config.json (50 layers, 4 KV heads × 128, 40 sliding-window layers). 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 = 2048 bytes per token per layer × 10 full-attention layers + 40 sliding-window layers capped at 513 tokens (FP16 cache, one sequence).config.json ↗
Smallest configuration per GPU · 110 GPUs · cheapest eligible on-demand offer per GPU count, every provider on fileWhat 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 3090Best fit | 4× 24 GB96 GB total | 99% tight | $0.25 | $183 | Clore.aiTrust 46 · read Nov 1 · receipt · also Vast.ai $0.58, Lium $0.88 |
| Tesla V100 32GB | 4× 32 GB128 GB total | 74% | $0.27 | $196 |  Vast.aiTrust 78 · read Nov 1 · receipt · also Clore.ai $0.41, Cyfuture AI $2.22 |
| RTX A6000 | 2× 48 GB96 GB total | 99% tight | $0.38 | $274 | Clore.aiTrust 46 · read Nov 1 · receipt · also Lium $0.84, Spheron $0.89 |
| Tesla P100 | 8× 16 GB128 GB total | 74% | $0.38 | $274 | Vast.aiTrust 78 · read Nov 1 · receipt |
| RTX 3060 | 8× 12 GB96 GB total | 99% tight | $0.43 | $312 | Vast.aiTrust 78 · read Nov 1 · receipt |
| Quadro RTX 8000 | 2× 48 GB96 GB total | 99% tight | $0.51 | $371 | Vast.aiTrust 78 · read Nov 1 · receipt |
| L40 | 2× 48 GB96 GB total | 99% tight | $0.54 | $391 | Vast.aiTrust 78 · read Nov 1 · receipt · also io.net $1.32, GPU.ai $1.56 |
| RTX PRO 6000 | 1× 96 GB96 GB total | 99% tight | $0.66 | $482 |  packet.ainot scored · read Nov 1 · receipt · also Runpod $0.69, HyperAI $0.80 |
| Titan RTX | 4× 24 GB96 GB total | 99% tight | $0.70 | $508 | Vast.aiTrust 78 · read Nov 1 · receipt |
| Tesla V100 16GB | 8× 16 GB128 GB total | 74% | $0.75 | $547 | Vast.aiTrust 78 · read Nov 1 · receipt · also CloudRift $2.32, Lambda $6.32 |
| RTX 5060 Ti | 8× 16 GB128 GB total | 74% | $0.75 | $549 | Vast.aiTrust 78 · read Nov 1 · receipt · also Clore.ai $0.82, Edgevana $1.51 |
| L40S | 2× 48 GB96 GB total | 99% tight | $0.76 | $555 |  LiumTrust 40 · read Nov 1 · receipt · also Vast.ai $1.28, VoltageGPU $1.52 |
| RTX A5000 | 4× 24 GB96 GB total | 99% tight | $0.80 | $586 | Vast.aiTrust 78 · read Nov 1 · receipt |
| RTX PRO 4000 | 4× 24 GB96 GB total | 99% tight | $0.83 | $608 | Clore.aiTrust 46 · read Nov 1 · receipt |
| RTX A4000 | 8× 16 GB128 GB total | 74% | $0.96 | $701 | LiumTrust 40 · read Nov 1 · receipt · also Vast.ai $0.96, VoltageGPU $1.92 |
| A800 80GB PCIe | 2× 80 GB160 GB total | 59% | $0.96 | $702 | Vast.aiTrust 78 · read Nov 1 · receipt |
| RTX PRO 5000 | 2× 48 GB96 GB total | 99% tight | $0.96 | $702 | Vast.aiTrust 78 · read Nov 1 · receipt · also Lium $1.70, AtmosCompute $2.04 |
| RTX 4070 Super | 8× 12 GB96 GB total | 99% tight | $0.97 | $708 | Vast.aiTrust 78 · read Nov 1 · receipt · also Edgevana $1.23, AtmosCompute $1.75 |
| RTX 6000 Ada | 2× 48 GB96 GB total | 99% tight | $0.99 | $722 | Vast.aiTrust 78 · read Nov 1 · receipt · also GPU.ai $1.30, io.net $1.37 |
| Gaudi 2 | 1× 96 GB96 GB total | 99% tight | $1.06 | $777 |  Cyfuture AInot scored · read Nov 1 · receipt |
| L4 | 4× 24 GB96 GB total | 99% tight | $1.09 | $795 |  Infomaniaknot scored · read Nov 1 · receipt · also Vast.ai $1.39, OVHcloud $3.35 |
| A100 80GB | 2× 80 GB160 GB total | 59% | $1.20 | $878 | Vast.aiTrust 78 · read Nov 1 · receipt · also Thunder Compute $2.18, Spheron $2.38 |
| RTX 5070 | 8× 12 GB96 GB total | 99% tight | $1.21 | $882 | Clore.aiTrust 46 · read Nov 1 · receipt |
| RTX 4090 | 4× 24 GB96 GB total | 99% tight | $1.25 | $913 | Clore.aiTrust 46 · read Nov 1 · receipt · also Vast.ai $1.28, Lium $2.00 |
| The 24 cheapest of 60 GPU types that fit. Every GPU, any precision and context, in the finder | |||||
Kolibri-1 · related