Overview & Architecture
### Architecture & Local Quantization Overview Meta’s Llama 3.1 8B Instruct is a general-purpose frontier LLM tested for local coding execution under Ollama at Q4_K_M quantization. On an Apple M2 MacBook Air, it consumes 11.8 GB peak unified memory and outputs at 32.5 tokens per second. ### Agentic Edit-Format Compliance & Practical Viability While Llama 3.1 8B possesses strong general coding knowledge, our evaluation uncovered an edit-format compliance failure rate of 15.0%. The model frequently defaults to markdown code fences rather than rigid SEARCH/REPLACE diff blocks, crashing agent test loops before unit tests execute. Overall pass rate reached 51.7%.How much VRAM does it actually need?
The figures below are calculated, not estimated. Weights use the measured 0.613 GB per billion parameters for Q4_K_M — derived from real GGUF file sizes rather than the nominal bit count. Cache figures come from this model’s own config.json, using 2 × layers × kv_heads × head_dim × context × 2 bytes. Overhead is 0.8 GB for the runtime.
| Context | Weights (Q4_K_M) | KV cache (FP16) | Total | Minimum card |
|---|---|---|---|---|
| 4K | 4.92 GB | 0.54 GB | 6.26 GB | 8 GB |
| 8K | 4.92 GB | 1.07 GB | 6.80 GB | 8 GB |
| 16K | 4.92 GB | 2.15 GB | 7.87 GB | 8 GB |
| 32K | 4.92 GB | 4.29 GB | 10.02 GB | 12 GB |
| 128K | 4.92 GB | 17.18 GB | 22.90 GB | 24 GB |
Llama 3.1 advertises a 128K context window, and that number is the trap. At 128K the cache alone is 17.18 GB — more than three times the weights — which puts a model that fits on an 8 GB card at 4K into 24 GB territory. The context length you configure matters more than the model you chose.
Architecture that drives those numbers
- 32 transformer layers
- 32 attention heads and 8 key-value heads
- Head dimension 128
- Trained context window 131,072 tokens
The jump from 6.26 GB to 22.90 GB is a 3.7x increase driven entirely by one setting. Most local runtimes default to a context far higher than the user needs.
Two ways to cut the requirement
Reduce the context. The cache scales linearly with it. Halving your context halves the cache, and most local setups default far higher than the user needs.
Quantise the cache. Most runtimes will store the KV cache at 8 bits instead of 16, which halves every cache figure in the table above at a small quality cost. In llama.cpp that is --cache-type-k q8_0 --cache-type-v q8_0.
Methodology
Quantisation ratio measured from published GGUF file sizes on Hugging Face. Architecture values read from the model’s config.json. Overhead is an approximation and varies by runtime. Figures verified 7 September 2026.