Llama 3 8B Lunaris
Sao10K · released Jun 26, 2024 · Sao10K/L3-8B-Lunaris-v1
- Type
- Open weightsLlama 3 Community License
- Params
- 8B
- Context
- 8K
about 6K words of context · download allowed, licence restricts use
Our take
Written Sep 5, 2026Llama 3 8B Lunaris is a small, community-fine-tuned text model released in 2024 with open-but-restricted weights. It offers some of the cheapest text generation rates we track, with consistently fast throughput across measured hosts.
Pick this when you need a tiny, low-cost text model for short requests and can accept Meta's licence restrictions. It suits low-latency workloads where 8,192 tokens is enough context. Skip it if you need verified quality scores, a permissive licence, or longer context windows.
The case for it
- Extremely low inference cost: all five tracked offers cluster within a cent of each other, with no premium tier.
- Consistently fast throughput at 56–71 tokens per second across four measured providers.
The case against it
- No benchmark scores in our data — chat, reasoning, coding and safety are all unverified.
- 8,192-token request limit is the only context length we hold for this model.
- Llama 3 Community Licence carries Meta's usage restrictions, not the freedom of Apache or MIT.
How good is it?
We hold no score for this model.
So there is no figure here for everyday use, coding, agent work or writing. That is a gap in our data, not a low score.
Can you run it yourself?
Comfortable fit
GeForce RTX 4090 · 24 GB
Room to spare. 16.1 GB spare means a 10% error in the size would not change the answer.
GeForce RTX 5090 · 32 GB
Room to spare. 24.1 GB spare means a 10% error in the size would not change the answer.
Apple M1 (8-core GPU) · 16 GB
Room to spare. 5.3 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
What is quantisation? →This model on every device we track71 devicesThe Q4 build most people download, on each device: what the weights come to, how much context the memory leaves, and whether it runs. Smallest device that runs it first.
Check against your own machine → · Where to rent it hosted →
Or rent it from someone else
Prices checked 2 hours ago — each listing carries its own date.
- per 1M tokens
- $0.040 in / $0.050 out
- Context served
- 8K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouterOpenRouter's own listing | $0.040 / $0.050checked 2 hours ago | 8K | not measured | Unknown | Unknown | Unknown |
| DeepInfraturbo tierfp8Through OpenRouter | $0.040 / $0.050checked 2 hours ago | 8K7K max reply | 73 tok/s | No | No | Unknown |
| Novita AIbf16Through OpenRouter | $0.050 / $0.050checked 2 hours ago | 8K7K max reply | 38 tok/s | No | No | Confirmed |
| Parasailbf16Through OpenRouter | $0.040 / $0.050checked 2 hours ago | 8K7K max reply | 31 tok/s | No | No | Confirmed |
DeepInfra
Novita AIAcross the 4 listings we hold: 3 say they do not train on prompts, 0 say they do and 1 does not say. 2 appear in the zero-retention registry we check; the rest are unknown to us.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| OpenRouterOpenRouter's own listing | ✗ | ✓ | ✓ |
| DeepInfraturbo · fp8Through OpenRouter | ✗ | ✗ | ✗ |
| Novita AIbf16Through OpenRouter | ✗ | ✓ | ✓ |
| Parasailbf16Through OpenRouter | ✗ | ✓ | ✓ |
Tool calling: 0 of 4 listings say yes, 4 say no. JSON output: 3 of 4 listings say yes, 1 says no. Strict schema: 3 of 4 listings say yes, 1 says no.
When we formed this view
Recent changes
What moved
first indexed by our pipelineEach date is the day we first saw the change, or the day the maker announced it.
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- No independent board has scored it, so we hold no quality figures at all.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 1 of 4 listings does not say whether it trains on prompts.
- We hold no cached-input rate for any of its listings.
- We hold no batch or off-peak rate for any of its listings.
- We hold a decode speed for it, but no prompt-processing (prefill) figure, so how long the input side of a job takes is unknown to us.
Licence and identifiers
What the licence allowsLlama 3 Community License, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
Llama 3 Community License
Commercial use allowed below 700M MAU; requires "Built with Meta Llama 3" attribution and Llama naming on derivatives.
Identifiers
- Hugging Face
- Sao10K/L3-8B-Lunaris-v1
- Architecture
- Dense
- Takes in, gives back
- Text in, text out
- Catalogue slug
- sao10k-llama-3-8b-lunaris