Overview & Architecture
### Architecture & Local Quantization Overview DeepSeek-Coder-6.7B-Instruct is a specialized code LLM trained from scratch on 2 trillion tokens of code and natural language. Evaluated under Ollama at Q4_K_M, the model requires 10.4 GB peak memory and generates at 36.2 tokens per second on Apple M2 Silicon. ### Agentic Edit-Format Compliance & Practical Viability In agentic diff testing, DeepSeek-Coder-6.7B recorded an edit-format failure rate of 11.7%, primarily driven by unclosed delimiter blocks on complex C++ and Rust exercises. When edits applied cleanly, its logical problem solving remained competitive, finishing with a 55.0% pass rate.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 | 4.13 GB | 2.15 GB | 7.08 GB | 8 GB |
| 8K | 4.13 GB | 4.29 GB | 9.23 GB | 12 GB |
| 16K | 4.13 GB | 8.59 GB | 13.52 GB | 16 GB |
This is the most misleading model on the list. It has the smallest weights here at 4.13 GB, and the largest cache by a wide margin. DeepSeek-Coder-6.7B predates grouped-query attention, so it stores 32 key-value heads instead of 4 or 8 — eight times what Qwen2.5-Coder-7B stores. At 16K context it needs 13.52 GB, more than Llama 3.1 8B needs at 32K.
Architecture that drives those numbers
- 32 transformer layers
- 32 attention heads and 32 key-value heads — no grouped-query attention
- Head dimension 128
- Trained context window 16,384 tokens
If you pick models by parameter count you will get this one wrong. It looks like the lightest option and behaves like one of the heaviest.
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.