fix(context): align guidance with 64k minimum

website/docs/guides/local-llm-on-mac.md

@@ -110,9 +110,9 @@ The `--cache-type-k q4_0 --cache-type-v q4_0` flags are the most important optim
 | q8_0 | ~8 GB |
 | **q4_0** | **~4 GB** |
 
-On an 8 GB Mac, use `q4_0` KV cache and reduce context to `-c 32768` (32K). On 16 GB, you can comfortably do 128K context. On 32 GB+, you can run larger models or multiple parallel slots.
+On an 8 GB Mac, use `q4_0` KV cache and choose a smaller model that can still fit Hermes' 64K minimum context. On 16 GB, you can comfortably do 128K context. On 32 GB+, you can run larger models or multiple parallel slots.
 
-If you're still running out of memory, reduce context size first (`-c`), then try a smaller quantization (Q3_K_M instead of Q4_K_M).
+If you're still running out of memory, reduce context only while staying at or above Hermes' 64K minimum; otherwise switch to a smaller model or smaller quantization (Q3_K_M instead of Q4_K_M).
 
 ### Test it
 

website/docs/guides/local-ollama-setup.md

@@ -156,19 +156,19 @@ Switch models on the fly inside a session:
 
 ### Increase Ollama's Context Window
 
-By default, Ollama uses a 2048-token context. For agentic work (tool calls, long conversations), you need more:
+By default, Ollama uses a 2048-token context. Hermes requires at least 64,000 tokens for agentic work with tools:
 
 ```bash
 # Create a Modelfile that extends context
 cat > /tmp/Modelfile << 'EOF'
 FROM gemma4:31b
-PARAMETER num_ctx 16384
+PARAMETER num_ctx 64000
 EOF
 
-ollama create gemma4-16k -f /tmp/Modelfile
+ollama create gemma4-64k -f /tmp/Modelfile
 ```
 
-Then update your Hermes config to use `gemma4-16k` as the model name.
+Then update your Hermes config to use `gemma4-64k` as the model name.
 
 ### Keep the Model Loaded
 
@@ -311,7 +311,7 @@ Your only cost is electricity — roughly $0.01–0.05 per session depending on
 ## What's Better with Cloud Models
 
 - **Very complex multi-step reasoning** — 70B+ or cloud models like Claude Opus are noticeably better
-- **Long context windows** — cloud models offer 100K–1M tokens; local models are typically 8K–32K
+- **Long context windows** — cloud models offer 100K–1M tokens; local runtimes often default below Hermes' 64K minimum unless you configure them
 - **Speed on large responses** — cloud inference is faster than CPU-only local for long generations
 
 The sweet spot: use local for everyday tasks, set up a cloud fallback for the hard stuff.