ANVE: the private AI office, on the record. See what that means
ANVEPrivate AI, answered · updated 2026-10-16
Short answer. An 8B model needs about 4.8 GB for its weights at 4-bit (Q4_K_M), 8.5 GB at 8-bit (Q8_0) and 16 GB at BF16, plus about 1.1 GB of KV cache per conversation at 8,192 tokens and 1 to 2 GB of overhead. That puts a 4-bit 8B model inside an 8 GB graphics card or a 16 GB laptop for one person, and an 8-bit one inside 16 GB. At very long context the cache can outgrow the model: 131,072 tokens on Llama 3.1 8B needs about 17 GB of cache, more than the weights. All figures are estimates.
Weights (GB) ≈ parameters in billions × bits per weight ÷ 8. The KV cache and about 2 GB of runtime overhead come on top. The estimate lands within about 2 per cent of the real file. GB means 10^9 bytes.
| Precision | Bits per weight | Weights for 8B (estimate) | Real file: Llama 3.1 8B in llama.cpp’s table |
|---|---|---|---|
| BF16 / F16 | 16 | 16.0 GB | 14.96 GiB, about 16.1 GB |
| Q8_0 | about 8.5 | 8.5 GB | 7.95 GiB, about 8.5 GB |
| Q4_K_M | about 4.8 | 4.8 GB | 4.58 GiB, about 4.9 GB (4.89 bits per weight) |
NVIDIA’s formula is 2 × layers × key/value heads × head size × bytes per value, per token. Meta’s Llama 3 paper lists 32 layers and 8 key/value heads for the 8B, with 128 values per head: 131,072 bytes per token. Qwen3-8B has 36 layers, 8 key/value heads and 128 values per head: 147,456 bytes per token.
| Context length | Llama 3.1 8B (FP16 cache) | Qwen3-8B (FP16 cache) |
|---|---|---|
| 8,192 tokens | 1.1 GB | 1.2 GB |
| 32,768 tokens | 4.3 GB | 4.8 GB |
| 131,072 tokens | 17.2 GB | 19.3 GB |
Ollama’s default context is only 4,096 tokens, so a model that seems to fit may need its context raised, and its memory with it, before it can read a long document.
| Memory available to the model | Best fit (Llama 3.1 8B) | Total | Verdict |
|---|---|---|---|
| 8 GB graphics card | Q4_K_M, 8,192 tokens: 4.8 + 1.1 + 2.0 | 7.9 GB | Tight; keep the context at 4,096 or shorter |
| 12 GB graphics card | Q8_0, 8,192 tokens: 8.5 + 1.1 + 2.0 | 11.6 GB | Fits with little to spare |
| 16 GB laptop or card | Q8_0, 32,768 tokens: 8.5 + 4.3 + 2.0 | 14.8 GB | Fits, and long documents work |
| 24 GB graphics card | BF16, 8,192 tokens: 16.0 + 1.1 + 2.0 | 19.1 GB | Fits at full precision |
An 8B model is the size where a single person’s laptop can help with drafting, summaries and extraction. In Vectara’s summarisation leaderboard of 22 September 2026, Qwen3-8B had a hallucination rate of 4.8 per cent, among the lower rates in the table for summarising documents. That measures one narrow task, not general quality.
ANVE’s One tier keeps a fast 4B model alongside its 27B-class default. For meeting minutes ANVE never uses a model smaller than 4B-class, because a 3B-class model failed ANVE’s test harness (see the note on the 4B question). Smaller is cheaper in memory, but there is a floor for work that must be right.
No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.
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