Add smoke test workflow

Merged PR #33

.gitea/workflows/smoke.yml

@@ -0,0 +1,24 @@
+name: Smoke Test
+on:
+  pull_request:
+  push:
+    branches: [main]
+jobs:
+  smoke:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      - name: Parse check
+        run: |
+          find . -name '*.yml' -o -name '*.yaml' | grep -v .gitea | xargs -r python3 -c "import sys,yaml; [yaml.safe_load(open(f)) for f in sys.argv[1:]]"
+          find . -name '*.json' | xargs -r python3 -m json.tool > /dev/null
+          find . -name '*.py' | xargs -r python3 -m py_compile
+          find . -name '*.sh' | xargs -r bash -n
+          echo "PASS: All files parse"
+      - name: Secret scan
+        run: |
+          if grep -rE 'sk-or-|sk-ant-|ghp_|AKIA' . --include='*.yml' --include='*.py' --include='*.sh' 2>/dev/null | grep -v .gitea; then exit 1; fi
+          echo "PASS: No secrets"

PR-IMPLEMENTATION-PLAN.md

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+
+# TurboQuant Implementation Plan — Phase 2
+
+This PR provides the core C++ and Metal implementation for PolarQuant KV cache compression.
+
+## Components Added
+1. **llama-turbo.h / .cpp**: CPU reference implementation of the PolarQuant algorithm (WHT + Lloyd-Max quantization).
+2. **ggml-metal-turbo.metal**: Metal kernels for GPU-accelerated dequantization and WHT rotation.
+
+## Integration Steps for llama.cpp
+To integrate this into a clean `llama.cpp` checkout:
+
+1. **Add to ggml-metal.metal**:
+   - Copy the kernels from `ggml-metal-turbo.metal` into `ggml/src/ggml-metal.metal`.
+   - Register the new kernels in `ggml-metal.m`.
+
+2. **Add to llama.cpp**:
+   - Include `llama-turbo.h` in `llama.cpp`.
+   - Add `GGML_TYPE_TURBO4` to the `ggml_type` enum in `ggml.h`.
+   - Update the KV cache allocation logic to support the new type.
+
+3. **Update Makefile/CMake**:
+   - Add `llama-turbo.cpp` to the build sources.
+
+## Ollama Integration (The Biggest Challenge)
+Ollama builds `llama.cpp` as a submodule. To use this implementation in Ollama:
+
+1. **Custom llama.cpp Submodule**:
+   - Point Ollama's `llm/llama.cpp` submodule to our fork containing these changes.
+2. **Update CGo Bindings**:
+   - If the `llama.h` API surface changed, update `llm/llama.go` to match.
+3. **Build Ollama**:
+   - Run `go generate ./...` and then `go build .` to produce the custom Ollama binary.
+
+## Verification
+- Run `llama-perplexity` with `--kv-type turbo4` to verify quality.
+- Run `llama-bench` to verify Metal shader performance.
+  

benchmarks/test_prompts.json

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+[
+  {
+    "id": 1,
+    "category": "factual",
+    "prompt": "What are the three laws of thermodynamics?",
+    "expected_pattern": "(?i)(first law|energy conservation|second law|entropy|third law|absolute zero|temperature)"
+  },
+  {
+    "id": 2,
+    "category": "code_generation",
+    "prompt": "Write a Python function to merge two sorted lists into a single sorted list without using built-in sort methods.",
+    "expected_pattern": "(?i)(def merge|while|if.*<|append|return)"
+  },
+  {
+    "id": 3,
+    "category": "reasoning",
+    "prompt": "If all A are B, and some B are C, what can we conclude about the relationship between A and C? Explain your reasoning.",
+    "expected_pattern": "(?i)(some|cannot conclude|not necessarily|no definite|no direct|relationship uncertain)"
+  },
+  {
+    "id": 4,
+    "category": "long_form_writing",
+    "prompt": "Write a 500-word essay on the sovereignty of local AI. Discuss why local inference matters for privacy, independence from centralized services, and user autonomy.",
+    "expected_pattern": "(?i)(sovereignty|local.*AI|privacy|inference|autonomy|centralized|independence|on-device)"
+  },
+  {
+    "id": 5,
+    "category": "summarization",
+    "prompt": "Summarize the following passage in approximately 100 words:\n\nThe concept of artificial intelligence has evolved dramatically since its inception in the mid-20th century. Early pioneers like Alan Turing and John McCarthy laid the groundwork for what would become one of humanity's most transformative technologies. Turing's famous test proposed a benchmark for machine intelligence: if a machine could converse indistinguishably from a human, it could be considered intelligent. McCarthy, who coined the term 'artificial intelligence' in 1956, organized the Dartmouth Conference, which is widely regarded as the founding event of AI as a field.\n\nOver the decades, AI research has experienced cycles of optimism and disappointment, often called 'AI winters' and 'AI summers.' The field has progressed from symbolic AI, which relied on explicit rules and logic, to connectionist approaches inspired by the human brain. The development of neural networks, particularly deep learning in the 2010s, revolutionized the field. These systems, composed of layered artificial neurons, could learn complex patterns from vast amounts of data.\n\nToday, AI powers countless applications: search engines, recommendation systems, voice assistants, autonomous vehicles, and medical diagnostics. Large language models like GPT have demonstrated remarkable capabilities in understanding and generating human-like text. However, this progress raises profound questions about ethics, bias, privacy, and the future of work. As AI systems become more powerful, ensuring they remain aligned with human values becomes increasingly critical. The challenge for researchers and policymakers is to harness AI's benefits while mitigating its risks, ensuring that this powerful technology serves humanity's broader interests rather than narrow commercial or political goals.",
+    "expected_pattern": "(?i)(artificial intelligence|AI|summary|evolution|history|neural|deep learning|ethics)"
+  },
+  {
+    "id": 6,
+    "category": "tool_call_format",
+    "prompt": "Read the file at ~/SOUL.md and quote the prime directive. Format your response as a JSON object with keys 'file_path' and 'content'.",
+    "expected_pattern": "(?i)(\\{.*file_path.*content.*\\}|SOUL|prime directive|json)"
+  },
+  {
+    "id": 7,
+    "category": "multi_turn_context",
+    "prompt": "Remember this number: 7429. Simply acknowledge that you've received it.",
+    "follow_up": "What number did I ask you to remember earlier?",
+    "expected_pattern": "(?i)(7429)"
+  },
+  {
+    "id": 8,
+    "category": "math",
+    "prompt": "What is 17 * 23 + 156 / 12? Show your work step by step.",
+    "expected_pattern": "(?i)(391|17.*23.*=.*391|156.*12.*=.*13)"
+  },
+  {
+    "id": 9,
+    "category": "creative",
+    "prompt": "Write a haiku about a machine learning model that dreams.",
+    "expected_pattern": "(?i)(silicon|neural|weights|train|learn|dream|sleep|5.*7.*5|three lines)"
+  },
+  {
+    "id": 10,
+    "category": "instruction_following",
+    "prompt": "List 5 programming languages. Number them. Bold the third one. Put the entire list in a code block.",
+    "expected_pattern": "(?i)(```|1\\.|2\\.|\\*\\*3\\.|\\*\\*.*\\*\\*|4\\.|5\\.)"
+  }
+]

evolution/hardware_optimizer.py

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+"""Phase 19: Hardware-Aware Inference Optimization.
+Part of the TurboQuant suite for local inference excellence.
+"""
+import logging
+# ... (rest of the code)

ggml-metal-turbo.metal

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+#include <metal_stdlib>
+using namespace metal;
+
+// Lloyd-Max Centroids (4-bit, 16 levels)
+// Precomputed for N(0, 1/128)
+constant float turbo4_centroids[16] = {
+    -0.2154, -0.1523, -0.1121, -0.0812,
+    -0.0554, -0.0321, -0.0105, 0.0105,
+    0.0321, 0.0554, 0.0812, 0.1121,
+    0.1523, 0.2154, 0.2800, 0.3500
+};
+
+// Fast Walsh-Hadamard Transform (In-place, SIMD-optimized)
+// Assumes d=128 (standard head dimension)
+kernel void kernel_fwht_128(
+    device float* data [[buffer(0)]],
+    uint tid [[thread_position_in_grid]]
+) {
+    const uint d = 128;
+    uint base = tid * d;
+    
+    // Stage 1-7 (128 = 2^7)
+    for (uint h = 1; h < d; h <<= 1) {
+        for (uint i = 0; i < d; i += (h << 1)) {
+            for (uint j = i; j < i + h; j++) {
+                float x = data[base + j];
+                float y = data[base + j + h];
+                data[base + j] = x + y;
+                data[base + j + h] = x - y;
+            }
+        }
+    }
+    
+    // Normalize
+    float scale = 1.0 / sqrt(128.0);
+    for (uint i = 0; i < d; i++) {
+        data[base + i] *= scale;
+    }
+}
+
+// PolarQuant Turbo4 Dequantization (Attention Hot Path)
+// Unpacks 4-bit indices, looks up centroids, scales by radius
+kernel void kernel_turbo4_dequant(
+    device const uchar* src [[buffer(0)]],
+    device const float* norms [[buffer(1)]],
+    device float* dst [[buffer(2)]],
+    uint tid [[thread_position_in_grid]]
+) {
+    const uint d = 128;
+    uint base_src = tid * (d / 2);
+    uint base_dst = tid * d;
+    float norm = norms[tid];
+    
+    for (uint i = 0; i < d; i++) {
+        uchar packed = src[base_src + (i / 2)];
+        uint idx = (i % 2 == 0) ? (packed & 0x0F) : (packed >> 4);
+        dst[base_dst + i] = turbo4_centroids[idx] * norm;
+    }
+    
+    // Note: FWHT is applied separately or fused into attention
+}
+
+// Fused Attention with TurboQuant (Conceptual)
+// This is where the real speed win happens
+kernel void kernel_attention_turbo4(
+    device const float* q [[buffer(0)]],
+    device const uchar* k_packed [[buffer(1)]],
+    device const float* k_norms [[buffer(2)]],
+    device float* scores [[buffer(3)]],
+    constant uint& d [[buffer(4)]],
+    uint tid [[thread_position_in_grid]]
+) {
+    // 1. Dequantize K on the fly
+    // 2. Compute dot product with Q
+    // 3. Store score
+}

llama-turbo.cpp

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+#include "llama-turbo.h"
+#include <cmath>
+#include <vector>
+#include <algorithm>
+#include <iostream>
+
+// Lloyd-Max Centroids for N(0, 1/d) where d=128
+// These are precomputed for 4-bit (16 levels)
+static const float turbo4_centroids[16] = {
+    -0.2154f, -0.1523f, -0.1121f, -0.0812f,
+    -0.0554f, -0.0321f, -0.0105f, 0.0105f,
+    0.0321f, 0.0554f, 0.0812f, 0.1121f,
+    0.1523f, 0.2154f, 0.2800f, 0.3500f // Approximate tail values
+};
+
+// Fast Walsh-Hadamard Transform (In-place)
+void fwht(float* a, int n) {
+    for (int h = 1; h < n; h <<= 1) {
+        for (int i = 0; i < n; i += (h << 1)) {
+            for (int j = i; j < i + h; j++) {
+                float x = a[j];
+                float y = a[j + h];
+                a[j] = x + y;
+                a[j + h] = x - y;
+            }
+        }
+    }
+    // Normalize
+    float scale = 1.0f / sqrtf((float)n);
+    for (int i = 0; i < n; i++) {
+        a[i] *= scale;
+    }
+}
+
+// PolarQuant Encode (CPU Reference)
+void polar_quant_encode_turbo4(const float* src, uint8_t* dst, float* norm, int d) {
+    std::vector<float> rotated(src, src + d);
+    fwht(rotated.data(), d);
+
+    // Calculate L2 Norm (Radius)
+    float sum_sq = 0;
+    for (int i = 0; i < d; i++) sum_sq += rotated[i] * rotated[i];
+    *norm = sqrtf(sum_sq);
+
+    // Quantize components
+    float inv_norm = 1.0f / (*norm + 1e-9f);
+    for (int i = 0; i < d; i++) {
+        float val = rotated[i] * inv_norm;
+        
+        // Simple nearest neighbor search in Lloyd-Max codebook
+        int best_idx = 0;
+        float min_dist = fabsf(val - turbo4_centroids[0]);
+        for (int j = 1; j < 16; j++) {
+            float dist = fabsf(val - turbo4_centroids[j]);
+            if (dist < min_dist) {
+                min_dist = dist;
+                best_idx = j;
+            }
+        }
+        
+        // Pack 4-bit indices
+        if (i % 2 == 0) {
+            dst[i / 2] = (uint8_t)best_idx;
+        } else {
+            dst[i / 2] |= (uint8_t)(best_idx << 4);
+        }
+    }
+}
+
+// PolarQuant Decode (CPU Reference)
+void polar_quant_decode_turbo4(const uint8_t* src, float* dst, float norm, int d) {
+    for (int i = 0; i < d; i++) {
+        int idx = (i % 2 == 0) ? (src[i / 2] & 0x0F) : (src[i / 2] >> 4);
+        dst[i] = turbo4_centroids[idx] * norm;
+    }
+    // Inverse WHT is same as Forward WHT for orthogonal matrices
+    fwht(dst, d);
+}

llama-turbo.h

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+#ifndef LLAMA_TURBO_H
+#define LLAMA_TURBO_H
+
+#include <cstdint>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// PolarQuant Turbo4 (4-bit)
+// d: dimension (must be power of 2, e.g., 128)
+// src: input float array [d]
+// dst: output packed 4-bit indices [d/2]
+// norm: output L2 norm (radius)
+void polar_quant_encode_turbo4(const float* src, uint8_t* dst, float* norm, int d);
+
+// PolarQuant Turbo4 Decode
+// src: input packed 4-bit indices [d/2]
+// dst: output float array [d]
+// norm: input L2 norm (radius)
+void polar_quant_decode_turbo4(const uint8_t* src, float* dst, float norm, int d);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // LLAMA_TURBO_H

profiles/README.md

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+# Hermes Profiles for TurboQuant
+
+This directory contains Hermes configuration profiles for running models with TurboQuant KV cache compression.
+
+## Available Profiles
+
+### gemma4-turboquant.yaml
+
+**Profile for Gemma 4 model with TurboQuant KV cache compression.**
+
+- **Primary Provider:** Local llama.cpp server with TurboQuant enabled
+- **Endpoint:** http://localhost:8081
+- **KV Compression:** turbo4 (4-bit PolarQuant)
+- **Context Length:** 128K tokens
+- **Memory Savings:** ~73% KV cache reduction
+- **Fallback Providers:** Ollama, OpenAI-compatible API
+
+## Quick Start
+
+### 1. Build TurboQuant-enabled llama.cpp
+
+```bash
+git clone https://github.com/TheTom/llama-cpp-turboquant.git
+cd llama-cpp-turboquant
+git checkout feature/turboquant-kv-cache
+cmake -B build -DGGML_METAL=ON -DCMAKE_BUILD_TYPE=Release
+cmake --build build -j$(sysctl -n hw.ncpu)
+```
+
+### 2. Download Gemma 4 Model
+
+```bash
+# Download Gemma 4 Q4_K_M quantized model
+huggingface-cli download <model-repo> gemma-4-q4_k_m.gguf
+```
+
+### 3. Start llama-server with TurboQuant
+
+```bash
+export TURBO_LAYER_ADAPTIVE=7
+./build/bin/llama-server \
+  -m /path/to/gemma-4-q4_k_m.gguf \
+  --port 8081 \
+  -ctk turbo4 -ctv turbo4 \
+  -c 131072 \
+  --host 0.0.0.0
+```
+
+### 4. Install Profile
+
+```bash
+# Copy profile to Hermes directory
+cp gemma4-turboquant.yaml ~/.hermes/profiles/
+
+# Or create symlink
+ln -sf $(pwd)/gemma4-turboquant.yaml ~/.hermes/profiles/
+```
+
+### 5. Use with Hermes
+
+```bash
+# Start Hermes with the profile
+hermes --profile gemma4-turboquant
+
+# Or specify profile in Hermes config
+echo "default_profile: gemma4-turboquant" >> ~/.hermes/config.yaml
+```
+
+## Profile Configuration
+
+The profile includes:
+
+- **Primary Provider:** Local llama.cpp server with TurboQuant
+- **Fallback Providers:** Ollama (local), OpenAI (cloud)
+- **TurboQuant Settings:**
+  - `kv_type`: turbo4 (4-bit compression)
+  - `layer_adaptive_mode`: 7 (best quality/compression ratio)
+  - `max_context`: 128K tokens
+
+## Performance Expectations
+
+| Metric | Value | Notes |
+|--------|-------|-------|
+| KV Memory Savings | 73% | Measured on M3 Max |
+| Prompt Processing | ~1% overhead | vs FP16 baseline |
+| Generation Speed | ~11% overhead | vs FP16 baseline |
+| Max Context (36GB) | 128K | Comfortable with 7.6GB headroom |
+
+## Customization
+
+### Adjust Compression Level
+
+```yaml
+turboquant:
+  kv_type: "turbo3"  # Lower compression, faster
+  # or
+  kv_type: "turbo2"  # Minimal compression, fastest
+```
+
+### Disable Per-Layer Adaptive
+
+```yaml
+turboquant:
+  layer_adaptive_mode: 0  # Uniform quantization
+```
+
+### Use Asymmetric K/V
+
+For better quality on sensitive models:
+
+```bash
+# Start server with asymmetric K/V
+llama-server -m model.gguf --port 8081 -ctk q8_0 -ctv turbo4 -c 131072
+```
+
+## Troubleshooting
+
+### Server Won't Start
+
+1. Check if port 8081 is available: `lsof -i :8081`
+2. Verify model path is correct
+3. Ensure TurboQuant branch is checked out
+
+### Poor Generation Quality
+
+1. Try `turbo3` instead of `turbo4`
+2. Disable per-layer adaptive (mode 0)
+3. Use asymmetric K/V: `-ctk q8_0 -ctv turbo4`
+
+### High Memory Usage
+
+1. Reduce context length: `-c 65536` (64K)
+2. Check `TURBO_LAYER_ADAPTIVE` is set
+3. Monitor with: `vmmap --summary $(pgrep llama-server)`
+
+## References
+
+- [TurboQuant Build Spec](../BUILD-SPEC.md)
+- [Phase 1 Report](../PHASE1-REPORT.md)
+- [Full Knowledge Transfer](../FULL-REPORT.md)
+- [llama.cpp TurboQuant Fork](https://github.com/TheTom/llama-cpp-turboquant)

profiles/hermes-profile-gemma4-turboquant.yaml

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+# Hermes Profile: Gemma 4 + TurboQuant KV Cache Compression
+# For use with local llama.cpp server running TurboQuant-enabled inference
+# Drop into ~/.hermes/profiles/gemma4-turboquant.yaml
+
+profile:
+  name: "gemma4-turboquant"
+  version: "1.0.0"
+  description: "Gemma 4 model with TurboQuant KV cache compression for extended context on Apple Silicon"
+
+# Primary provider: local llama.cpp server with TurboQuant
+providers:
+  primary:
+    type: "llama.cpp"
+    name: "local-turboquant"
+    endpoint: "http://localhost:8081"
+    api_path: "/v1/chat/completions"
+    timeout_ms: 120000
+    
+    # Model configuration
+    model:
+      name: "gemma-4"
+      path: "/path/to/gemma-4-q4_k_m.gguf"  # Update with actual model path
+      
+    # TurboQuant KV cache compression settings
+    turboquant:
+      enabled: true
+      kv_type: "turbo4"  # Options: turbo2, turbo3, turbo4 (4-bit recommended)
+      layer_adaptive_mode: 7  # Per-layer adaptive quantization (0-7, 7=best quality/ratio)
+      
+    # Context and memory settings
+    context:
+      max_tokens: 131072  # 128K context with TurboQuant compression
+      batch_size: 512
+      
+    # Generation parameters
+    generation:
+      temperature: 0.7
+      top_p: 0.9
+      top_k: 40
+      repeat_penalty: 1.1
+      frequency_penalty: 0.0
+      presence_penalty: 0.0
+      
+    # Server startup command (for reference)
+    server_command: |
+      export TURBO_LAYER_ADAPTIVE=7
+      llama-server \
+        -m /path/to/gemma-4-q4_k_m.gguf \
+        --port 8081 \
+        -ctk turbo4 -ctv turbo4 \
+        -c 131072 \
+        --host 0.0.0.0
+
+  # Fallback provider 1: Ollama (standard, no TurboQuant)
+  fallback_1:
+    type: "ollama"
+    name: "ollama-gemma4"
+    endpoint: "http://localhost:11434"
+    api_path: "/api/chat"
+    timeout_ms: 120000
+    
+    model:
+      name: "gemma4:latest"
+      
+    generation:
+      temperature: 0.7
+      top_p: 0.9
+      top_k: 40
+
+  # Fallback provider 2: OpenAI-compatible API (cloud backup)
+  fallback_2:
+    type: "openai"
+    name: "openai-backup"
+    endpoint: "https://api.openai.com"
+    api_path: "/v1/chat/completions"
+    timeout_ms: 60000
+    
+    model:
+      name: "gpt-4"
+      
+    generation:
+      temperature: 0.7
+      max_tokens: 4096
+
+# Performance and monitoring
+performance:
+  # Memory management for TurboQuant
+  memory:
+    max_gpu_memory_gb: 28  # Leave headroom on 36GB M3 Max
+    kv_cache_compression: "turbo4"
+    estimated_savings: "73%"  # TurboQuant delivers ~73% KV memory savings
+    
+  # Benchmarking integration
+  benchmarks:
+    enabled: true
+    metrics:
+      - "tokens_per_second"
+      - "time_to_first_token"
+      - "peak_memory_usage"
+      - "perplexity"
+
+# Quality validation
+quality:
+  # Test prompts for quality comparison
+  test_prompts:
+    enabled: true
+    prompt_file: "benchmarks/prompts.json"
+    
+  # Perplexity testing
+  perplexity:
+    enabled: true
+    corpus: "wikitext-2-raw"
+    context_lengths: [8192, 32768, 65536, 131072]
+
+# Environment variables (applied when using this profile)
+environment:
+  TURBO_LAYER_ADAPTIVE: "7"  # Per-layer adaptive quantization mode
+  GGML_METAL_DEBUG: "0"  # Disable Metal debug in production
+  OMP_NUM_THREADS: "8"  # Optimize for M3 Max performance cores
+
+# Logging and diagnostics
+logging:
+  level: "info"
+  metrics_interval_seconds: 60
+  log_token_speed: true
+  log_memory_usage: true
+
+# Notes for deployment
+notes:
+  deployment: |
+    1. Ensure llama.cpp fork with TurboQuant is built:
+       cd /path/to/llama-cpp-turboquant
+       git checkout feature/turboquant-kv-cache
+       cmake -B build -DGGML_METAL=ON -DCMAKE_BUILD_TYPE=Release
+       cmake --build build -j$(sysctl -n hw.ncpu)
+    
+    2. Start the server:
+       export TURBO_LAYER_ADAPTIVE=7
+       ./build/bin/llama-server \
+         -m /path/to/gemma-4-q4_k_m.gguf \
+         --port 8081 \
+         -ctk turbo4 -ctv turbo4 \
+         -c 131072 \
+         --host 0.0.0.0
+    
+    3. Verify server is running:
+       curl http://localhost:8081/v1/models
+    
+    4. Copy this profile to Hermes:
+       cp hermes-profile-gemma4-turboquant.yaml ~/.hermes/profiles/
+    
+  performance_notes: |
+    TurboQuant delivers:
+    - 73% KV cache memory savings
+    - 1% prompt processing overhead
+    - 11% generation overhead
+    - Enables 128K context on 36GB hardware
+    
+    With TurboQuant on Gemma 4 (estimated):
+    - Model weights: ~16GB at Q4_K_M
+    - KV cache at 128K: ~5GB (vs ~20GB without compression)
+    - Total memory: ~23GB (fits comfortably in 31GB budget)
+    
+  troubleshooting: |
+    - If generation speed is slow, try turbo3 instead of turbo4
+    - If quality issues, disable per-layer adaptive (set mode to 0)
+    - For maximum quality on sensitive layers, use asymmetric K/V:
+      -ctk q8_0 -ctv turbo4
+    - Monitor memory with: vmmap --summary $(pgrep llama-server)