# Thesis The transition from "hobbyist inference" to "production-grade local intelligence" represents the current frontier of the home-lab movement. For years, the primary hurdle for local LLM deployment was the simple binary of feasibility: *Can I run this model on my hardware?* With the democratization of GGUF quantization, the answer to that question has become a resounding "yes" for a vast majority of the community. However, we are now entering a more nuanced era of evaluation. The question is no longer just about feasibility, but about **operational reliability and performance profiling.** This paper explores the capabilities of a 32GB-class R9700 server—a mid-to-high-tier home-lab powerhouse—when tasked with running state-of-the-art open-weight models, specifically the **Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf** variant. By utilizing `llama.cpp` over a Vulkan backend, leveraging a massive 262,144-token context window, and employing Multi-Token Prediction (MTP) draft decoding, we aim to establish a baseline for high-performance local reasoning. Central to this exploration is the implementation of a **Local AI Flight Recorder**. By intercepting OpenAI-compatible proxy traffic, we can capture the "black box" telemetry of every inference pass—metadata, timings, usage, and response previews. This allows us to move beyond subjective "vibes" and toward empirical data. We seek to answer: How does a 35B-parameter model behave when pushed to its reasoning limits? How does MTP draft decoding affect the trade-off between latency and coherence? And most importantly, how does a high reasoning budget (8192) impact the utility of the model in complex, multi-step workflows like agentic work and long-context RAG? # Hardware And Runtime The testing environment is centered on a 32GB-class R9700 server. In the context of local LLM hosting, "32GB-class" refers to the available high-speed memory (VRAM/Unified Memory) available for the model weights and the KV (Key-Value) cache. For a 35B parameter model, this represents a significant engineering challenge. ### The Model: Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf The selection of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf model is intentional. This model is designed for high-efficiency reasoning, utilizing an architecture that balances parameter count with "intelligence density." The "Balanced" quantization ensures that we retain the majority of the model's nuance while fitting within the memory constraints of our hardware. The inclusion of MTP (Multi-Token Prediction) support is critical, as it allows for speculative decoding techniques that can significantly accelerate output generation without sacrificing the quality of the reasoning chain. ### The Runtime: llama.cpp and Vulkan To maximize the utility of the R9700, we utilize `llama.cpp`. While CUDA remains the gold standard for NVIDIA hardware, the Vulkan backend provides a highly portable and performant alternative that allows us to tap into the server's GPU capabilities with minimal overhead. The runtime configuration is tuned for high-throughput reasoning: - **Context Window:** 262,144 tokens. This is an aggressive target for a 35B model. It requires careful management of the KV cache to ensure that the memory footprint does not exceed the 32GB limit while still allowing for massive document ingestion. - **MTP Draft Decoding:** We enable Multi-Token Prediction to allow the model to "guess" subsequent tokens during the decoding phase. This is particularly effective for models trained with MTP, as it reduces the number of full forward passes required for each token generated. - **Reasoning Budget:** We set `--reasoning-budget 8192`. This parameter is vital for models that utilize internal "Chain of Thought" (CoT) processing. It allows the model to allocate up to 8,192 tokens of internal monologue before producing a final answer, ensuring that complex problems are "thought through" rather than rushed. # Why Thinking Budgets Matter The rise of "reasoning" models has fundamentally changed how we interact with LLMs. Unlike standard chat models that aim for the most probable next token, reasoning models are trained to explore a space of possibilities before committing to a final output. The `reasoning-budget` is the dial that controls this exploration. When we set a budget of 8,192, we are providing the model with a vast "scratchpad." For a 35B model, this is the difference between a model that gives a correct answer because it "knows" the fact, and a model that gives a correct answer because it correctly deduced the logic required to find the fact. In a home-lab environment, this creates a unique performance profile. A high reasoning budget increases the **Time to First Token (TTFT)** for the final answer because the model is performing extensive internal computation. However, it significantly increases the **Success Rate** on complex tasks. Without a sufficient budget, the model may "hallucinate" a conclusion because it was forced to stop its internal deliberation too early. By capturing this via the Flight Recorder, we can observe the "Thinking" phase—the internal tokens that the model generates to arrive at its conclusion—and measure the correlation between the length of this internal monologue and the accuracy of the final response. # Why Toy Benchmarks Failed Early iterations of this benchmark suite were discarded because they were fundamentally flawed. We initially attempted to run standard "Hello World" prompts—short questions with short expected answers—and observed high performance. However, these results were invalid for two primary reasons: 1. **Max_tokens Truncation:** Many of our early tests used a standard `max_tokens` limit (e.g., 512 or 1024). For a reasoning model with an 8,192-token budget, a 512-token limit forces the model to cut off its "thinking" process before it even reaches the final answer. The model was essentially being asked to solve a complex math problem but was forced to stop speaking before it could finish the calculation. 2. **Lack of Stress:** A "toy" prompt does not stress the KV cache or the MTP decoding logic. To truly understand what a 32GB R9700 can do, we need to see how the model handles 50,000 tokens of context, how it maintains coherence over a 2,000-word technical explanation, and how it handles multi-step tool calls. The failure of toy benchmarks taught us that for reasoning models, **the output is only as good as the space provided for the thought.** If the `max_tokens` is smaller than the `reasoning-budget`, the model's performance will degrade exponentially as the complexity of the prompt increases. # The Real-World Test Suite To replace the failed toy benchmarks, we have designed a comprehensive suite that mirrors actual professional and power-user workflows. Each category is designed to push a specific capability of the Qwen3.6-35B model. ### 1. Coding (Repo-Level and Logic) Instead of "Write a Python script to sort a list," we are testing: - **Refactoring:** Providing a 200-line function and asking for a rewrite that improves time complexity while maintaining the same API. - **Bug Hunting:** Providing a snippet with a subtle race condition or memory leak and asking for a diagnosis. - **Unit Test Generation:** Providing a complex class and requiring a comprehensive suite of PyTest cases, including edge cases. ### 2. RAG (Retrieval Augmented Generation) We are testing the model's ability to synthesize information from a massive context window: - **Needle-in-a-Haystack:** Placing a specific, obscure fact in a 100,000-token document and asking the model to retrieve it. - **Multi-Document Synthesis:** Providing three different technical manuals and asking the model to create a unified "Quick Start Guide" based on conflicting information across the three. ### 3. Agentic Work (Tool Use and Planning) This tests the model's ability to act as a "brain" for other programs: - **Plan-then-Execute:** Giving a high-level goal (e.g., "Set up a Docker container for a Postgres DB with a specific volume mapping") and requiring the model to output a step-by-step plan before generating the commands. - **Tool Selection:** Providing a list of available API tools and asking the model to select the correct tool and format the JSON arguments correctly. ### 4. Server-Admin Work Testing the model's utility as a sysadmin assistant: - **Log Analysis:** Providing a 500-line `journalctl` output and asking the model to identify the root cause of a service failure. - **Config Generation:** Generating complex Nginx or HAProxy configurations based on specific network requirements. ### 5. Chat Assistant Behavior Evaluating the "personality" and nuance: - **Persona Consistency:** Maintaining a specific professional tone over a 10-turn conversation. - **Nuance Handling:** Asking the model to explain a complex philosophical concept to a child versus an expert. ### 6. Creative Writing Testing the model's "flow": - **Narrative Arc:** Generating a 1,000-word short story with a specific plot twist and consistent character voices. - **Style Mimicking:** Writing a technical description in the style of a specific famous author. ### 7. Long-Output Reliability Testing the "exhaustion" point: - **Extended Documentation:** Asking the model to write a 2,000-word technical specification. We are measuring where the model begins to repeat itself or lose the thread of the original requirements. ### 8. Context Fit Testing the limits of the 262,144 context window: - **Contextual Drift:** Measuring how the model's accuracy on a specific task degrades as we fill the context window from 10k to 100k to 200k tokens. ### 9. MTP vs. Non-MTP Behavior A controlled comparison: - We will run the same set of prompts twice: once with MTP draft decoding enabled and once with it disabled. We are looking for the "sweet spot" where MTP provides a significant speedup without introducing "hallucination drift" in the reasoning chain. # What To Measure The Flight Recorder will capture a suite of metrics for every single request in the test suite. We will not rely on "it felt faster." We will rely on: - **TTFT (Time to First Token):** How long does the user wait before the model starts "thinking" or "speaking"? - **TPOT (Time Per Output Token):** The average speed of the generation. - **Tokens Per Second (TPS):** The overall throughput. - **Reasoning Density:** The ratio of "thinking tokens" to "final answer tokens." - **MTP Speedup Factor:** The percentage increase in TPS when MTP is active vs. inactive. - **Contextual Accuracy Score:** A manual/automated score (0-10) on how well the model adhered to the provided context in RAG tasks. - **Success Rate (Pass@1):** For coding and server-admin tasks, did the output actually work? - **KV Cache Pressure:** Monitoring the memory overhead as the context window fills. # How To Read The Charts Once the data is collected, the results will be presented in several key visualizations. Users should interpret them as follows: - **The "Reasoning vs. Speed" Scatter Plot:** This will plot the length of the reasoning chain (X-axis) against the final answer accuracy (Y-axis). We expect to see a "diminishing returns" curve where, after a certain point, more thinking tokens do not lead to better answers. - **The "MTP Efficiency" Bar Chart:** A side-by-side comparison of TPS for different model sizes and quantizations with and without MTP. This will show the "ROI" of using MTP-capable models. - **The "Context Degradation" Line Graph:** This will show the accuracy of the model as the context window fills from 0 to 262,144 tokens. It will help identify the "cliff"—the point where the model begins to forget the beginning of the prompt. - **The "Flight Recorder" Latency Heatmap:** A visualization of TTFT and TPOT across different categories (Coding, RAG, etc.) to show which types of tasks are most "expensive" in terms of compute. # Privacy Boundaries Because this is a home-lab environment, the integrity of our data and the privacy of our local environment are paramount. 1. **Local Raw Data:** All raw logs from the Flight Recorder, including full prompt/response pairs and internal reasoning chains, will remain on the local R9700 server. These logs may contain sensitive information or unique configurations that we do not wish to share. 2. **Sanitization Protocol:** Before any data is shared publicly, it will undergo a sanitization process. This involves stripping out specific IP addresses, hostnames, and private keys from the logs. 3. **Aggregate Findings:** Only the *aggregate* metrics (averages, standard deviations, and anonymized success/failure counts) will be published. We will share the "What" and the "How Fast," but never the "What exactly was being processed." 4. **Benchmark Artifacts:** Any "artifacts" (like a generated script that failed) will be replaced with generic placeholders to ensure no private data leaks into the public domain. # Expected Model Categories Based on the current state of the GGUF ecosystem, we expect the Qwen3.6-35B model to fall into the following categories during our testing: - **High-Reasoning / Moderate-Speed:** Due to the 8,192 reasoning budget, we expect this model to be slower than a standard 7B or 14B model but significantly more capable in complex logic. - **Context-Heavy Specialist:** With the 262k context window, we expect the model to excel in RAG tasks where it can "digest" entire codebases or long legal documents. - **MTP-Optimized:** We expect the MTP draft decoding to provide a noticeable "boost" in TPOT, potentially making the 35B model feel as responsive as a smaller model during creative writing tasks. # Limits And Caveats No home-lab setup is perfect. We must acknowledge the following limitations: - **VRAM Bottlenecks:** At 262,144 context tokens, the KV cache can become massive. Even with a 32GB-class server, we may find that we have to trade off some "model size" (quantization level) to keep the entire context window in memory. - **Vulkan Overhead:** While Vulkan is capable, it may not reach the raw throughput of CUDA. Our benchmarks should be viewed as "high-performance Vulkan" rather than "theoretical maximum inference." - **MTP Drift:** In very long outputs (over 1,000 tokens), MTP draft decoding can occasionally "drift" into repetitive loops or lose coherence. We will be monitoring for these specific failure modes. - **Reasoning Budget Exhaustion:** If a prompt is so complex that it requires more than 8,192 tokens of "thought," the model may produce a sub-optimal answer. We will identify the "complexity ceiling" of the current budget. # Next Experiments Once this baseline is established, the next phase of research will involve: 1. **Quantization Sensitivity Analysis:** Testing the same suite on 4-bit, 6-bit, and 8-bit versions of the model to find the "accuracy-to-speed" sweet spot for the R9700. 2. **Multi-GPU Scaling:** If the 32GB limit is hit too quickly by the KV cache, we will explore splitting the model across dual-GPU setups to see if the increased VRAM allows for even larger context windows. 3. **Dynamic Reasoning Budgets:** Experimenting with a dynamic budget where the model "decides" how much to think based on the complexity of the prompt, rather than a fixed 8,192. 4. **Agentic Loop Integration:** Moving from "one-shot" agentic prompts to a multi-turn loop where the Flight Recorder tracks the model's ability to correct its own mistakes over 10+ turns of tool use. 5. **Dynamic Reasoning Budgets:** Experimenting with a dynamic budget where the model "decides" how much to think based on the complexity of the prompt, rather than a fixed 8,192. 6. **Quantization Sensitivity Analysis:** A systematic sweep of bit-depths (Q4_K_M, Q6_K, Q8_0) to identify the precise point where reasoning logic begins to degrade in the Qwen3.6-35B architecture. 7. **Contextual Pressure Testing:** Measuring the "Time to First Token" (TTFT) as a function of KV cache size, specifically looking for non-linear spikes in latency as we approach the 200k token mark. 8. **MTP Acceptance Rate Tracking:** Quantifying the percentage of draft tokens accepted by the primary model to determine the efficiency of the MTP head in different linguistic contexts (e.g., code vs. creative prose). # Deep Dive: The Mechanics of Multi-Token Prediction (MTP) To understand the performance profile of the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf, we must first dissect the mechanics of Multi-Token Prediction (MTP). Standard autoregressive decoding is inherently sequential: the model predicts token $n$, then uses that token to predict $n+1$. This creates a bottleneck where the speed of generation is limited by the number of sequential forward passes required. MTP shifts this paradigm by allowing the model to predict a sequence of future tokens simultaneously. In the context of `llama.cpp` and the APEX architecture, this is often implemented via speculative decoding. The model essentially "guesses" the next $k$ tokens (the draft) in a single forward pass. These draft tokens are then verified against the primary model's weights in a subsequent pass. If the draft is correct, we have effectively generated $k$ tokens in nearly the time it would have taken to generate one. For the Qwen3.6-35B model, the MTP head is specifically trained to anticipate the structure of reasoning chains. Because reasoning models often follow predictable linguistic patterns (e.g., "Step 1: ... Step 2: ..."), the MTP head can achieve high acceptance rates. However, the "Balanced" quantization presents a unique challenge here. We must ensure that the quantization doesn't "blur" the weights of the MTP head to the point where the draft tokens become incoherent, leading to a high rejection rate and a net loss in performance. Our Flight Recorder will specifically track the "Acceptance Ratio"—the ratio of accepted draft tokens to total tokens generated—to provide a clear picture of MTP's actual utility in a 32GB memory environment. # Memory Engineering: The 262k Context Challenge One of the most ambitious aspects of this benchmark is the 262,144-token context window. In the world of LLM inference, context is not "free." Every token added to the context increases the size of the Key-Value (KV) cache, which must reside in high-speed memory. The memory footprint of the KV cache can be estimated using the formula: $Memory = 2 \times \text{Layers} \times \text{Heads} \times \text{Head\_Dim} \times \text{Context\_Length} \times \text{Precision}$ For a 35B model, the number of layers and heads is substantial. At a context length of 262,144, the KV cache alone can consume a massive portion of the 32GB available. This creates a "zero-sum game" between model weights and context. If we use a high-precision quantization (like Q8_0), we may find ourselves unable to fit both the model and the full context window into memory. To navigate this, we are utilizing `llama.cpp`'s advanced memory management features, including: - **Flash Attention:** Reducing the memory complexity of the attention mechanism from quadratic to linear relative to the sequence length. - **KV Cache Quantization:** Compressing the KV cache itself (e.g., to 4-bit or 8-bit) to allow for larger context windows without a linear increase in memory usage. - **Context Shifting:** Ensuring that as new tokens are added, the oldest tokens are managed efficiently to prevent memory overflow. The Flight Recorder will monitor "Memory Pressure" in real-time. We want to identify the "Saturation Point"—the exact context length where the system begins to swap memory or where the KV cache causes a significant drop in Tokens Per Second (TPS). # Flight Recorder Architecture: Telemetry at Scale The "Local AI Flight Recorder" is the backbone of this empirical study. Rather than relying on manual observation, we have built a middleware layer that sits between the user's application (e.g., a web UI or a CLI tool) and the `llama.cpp` server. The recorder operates on the OpenAI-compatible API standard. Every time a request is sent to the `/v1/chat/completions` endpoint, the recorder intercepts the payload. It captures: 1. **Request Metadata:** The exact prompt, the `temperature`, `top_p`, `max_tokens`, and the `reasoning_budget`. 2. **Streamed Response Chunks:** For models that support streaming, the recorder captures every chunk as it is produced. This allows us to calculate the precise Time Per Output Token (TPOT) for every single word. 3. **Thinking Trace Extraction:** For reasoning models, the recorder identifies the "thought" block (the tokens generated within the reasoning budget) and separates them from the final answer. This allows us to analyze the "Reasoning Density"—how much "work" the model did behind the scenes versus what it actually told the user. 4. **Usage Metrics:** It records the final `prompt_tokens` and `completion_tokens` to calculate the total cost of the inference pass in terms of compute time. 5. **Benchmark Artifacts:** If a coding task is performed, the recorder captures the resulting code block. If the code fails to execute (verified via a local sandbox), the recorder flags this as a "Failure" in the Pass@1 metric. By centralizing this data, we can generate heatmaps of performance. For example, we can see if TPOT increases significantly when the reasoning budget exceeds 4,000 tokens, or if the acceptance rate of MTP drops when the context window exceeds 100,000 tokens. # Agentic Reasoning and the Chain-of-Thought (CoT) Loop The ultimate goal of using a 35B reasoning model in a home-lab is to move toward **Agentic Workflows**. An agent is an LLM that can use tools, plan multi-step tasks, and correct its own errors. This is where the 8,192 reasoning budget becomes a critical asset. In a standard "Greedy" model, if you ask the model to "Fix the permissions on the web server and then restart the service," the model might generate a command that is slightly incorrect. Without a reasoning step, it will proceed to the next step, potentially causing a cascading failure. With the Qwen3.6-35B and a high reasoning budget, the model can perform a "Plan-then-Execute" loop: 1. **Internal Monologue:** The model "thinks" about the permissions required, identifies the specific `chmod` and `chown` commands, and simulates the potential side effects. 2. **Tool Call Generation:** It outputs the first command. 3. **Observation:** The system executes the command and feeds the output back to the model. 4. **Correction:** The model "thinks" again about the output. If it sees an error, it uses its reasoning budget to diagnose the error and generate a corrected command. Our test suite specifically targets this behavior. We will measure "Agentic Success Rate"—the percentage of multi-step tasks completed successfully without human intervention. We will also use the Flight Recorder to visualize the "Reasoning Path," showing how the model's internal monologue changes as it encounters errors during the agentic loop. # Vulkan Backend Performance Dynamics While CUDA is the native language of AI, the Vulkan backend in `llama.cpp` is a vital tool for the home-lab enthusiast. It allows us to leverage a wider variety of hardware, including AMD GPUs and even some integrated graphics, while maintaining a high level of performance. However, Vulkan introduces a different set of performance characteristics than CUDA. We are specifically looking for: - **Memory Mapping Overhead:** How efficiently Vulkan handles the mapping of the large GGUF weights into the GPU's memory space. - **Kernel Execution Speed:** Comparing the speed of the matrix multiplications (MatMuls) on Vulkan versus the theoretical CUDA maximum. - **Context Switching:** How the Vulkan driver handles the rapid switching between the reasoning phase and the generation phase. By benchmarking on Vulkan, we provide a more "real-world" look at what a diverse home-lab setup can achieve. We want to know if the Vulkan backend can sustain the throughput required for a smooth "agentic" experience, or if the overhead becomes too great when the context window is pushed to its limits. # Quantization and the Fidelity Trade-off The "Balanced" quantization in the Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf is a strategic choice. In the GGUF ecosystem, quantization is a trade-off between the "Size" of the model and the "Fidelity" of its logic. For reasoning models, this trade-off is more sensitive than for standard chat models. A reasoning model relies on subtle weight distributions to maintain a coherent "Chain of Thought." If the quantization is too aggressive (e.g., 3-bit), the model may lose the ability to follow complex logic, regardless of how large the reasoning budget is. It's like giving a person a massive scratchpad but making them unable to understand the instructions written on it. Our benchmark will include a "Fidelity Check." We will compare the output of the "Balanced" model against a higher-precision version (if memory allows) on a set of difficult logic puzzles. This will help us determine if the "Balanced" quantization is sufficient for production-grade agentic work or if we need to scale up to a 64GB-class server to accommodate higher-bit weights. # Statistical Methodology for Evaluation To ensure the integrity of this draft, we will use a rigorous statistical approach to evaluate the results. We will not rely on "cherry-picked" examples of the model performing well. Instead, we will use: 1. **Pass@k Metrics:** For coding and server-admin tasks, we will measure how many of the first $k$ attempts produced a working solution. 2. **Standard Deviation of TPOT:** We will measure the consistency of the model's speed. A model that is fast on average but has high variance (stuttering) is less useful for real-time applications. 3. **Contextual Drift Coefficient:** We will measure the degradation of accuracy as a function of context length. We will use a "sliding window" of accuracy, measuring the model's performance on a 100-token fact-retrieval task at different positions within a 200,000-token document. 4. **Reasoning Efficiency Score:** A custom metric defined as: $RES = \frac{\text{Success Rate}}{\text{Average Reasoning Tokens}}$ This score will tell us how "efficiently" the model thinks. A high score means the model reaches the correct answer with a concise reasoning chain; a low score means it is "over-thinking" or "looping" internally without making progress. # Synthesis and Conclusion The goal of this research is to provide a definitive guide for the home-lab community on the capabilities of mid-to-high-tier hardware when paired with modern reasoning architectures. We are moving past the era of "can it run?" and into the era of "how well can it work?" By documenting the interplay between the R9700 server, the Qwen3.6-35B model, the 8,192 reasoning budget, and the MTP decoding, we aim to create a blueprint for local AI deployment. We want to show that with the right tools—like the Flight Recorder—and the right configurations—like Vulkan and MTP—the home lab can host intelligence that rivals commercial offerings while maintaining total privacy and operational control. This draft will serve as the foundation for our final report. As the data flows through the Flight Recorder and the charts are populated, we will refine these sections with hard numbers, identifying the specific "sweet spots" of the R9700 server. We will provide the community with the data they need to decide how to allocate their hardware, how to tune their `llama.cpp` parameters, and how to build the next generation of local AI agents. The data is coming. The recorder is running. The benchmark begins now. # Technical Appendix: Scenario Blueprints and Telemetry Schema To ensure the reproducibility of this benchmark and to provide a framework for future researchers, we have formalized the "Scenario Blueprints." These are not just prompts; they are structured test cases with predefined "Success Criteria" and "Failure Modes." ### Scenario Blueprint 1: The Legacy Migration (Coding) **Objective:** Evaluate the model's ability to perform complex architectural refactoring while maintaining logic integrity. **Input:** A 300-line Python script utilizing a deprecated, nested `if-else` structure for handling user permissions and a 50-line "Requirement Document" describing a move to a Decorator-based permission system. **Success Criteria:** 1. The output must correctly implement the Decorator pattern. 2. The output must pass a set of 5 provided unit tests (checking for specific permission denials). 3. The model must not introduce "hallucinated" dependencies (e.g., importing a library that doesn't exist). **Failure Modes:** - **Logic Regression:** The model successfully refactors the code but breaks the permission logic. - **Incomplete Migration:** The model only refactors the first 50 lines and leaves the rest in the old format. - **Reasoning Collapse:** The model begins the "thinking" process but fails to reach a coherent plan, resulting in a "broken" decorator. ### Scenario Blueprint 2: The Compliance Audit (RAG) **Objective:** Test "Needle-in-a-Haystack" retrieval and multi-document synthesis at high context volumes. **Input:** A 150,000-token corpus consisting of three distinct "Company Policies" (HR, IT Security, and Data Privacy). **The Query:** "Based on the IT Security policy and the Data Privacy guidelines, what are the specific steps a contractor must take to access the production database, and does this conflict with any HR policies regarding remote work?" **Success Criteria:** 1. The model must identify the specific clause in the IT Security policy. 2. The model must identify the specific conflict in the HR policy. 3. The model must synthesize a single, coherent answer that addresses both. **Failure Modes:** - **Contextual Drift:** The model provides a generic answer about database access without referencing the specific policies. - **Lost in the Middle:** The model correctly identifies the first policy but fails to retrieve information from the middle of the 150,000-token block. - **Hallucination:** The model invents a policy that does not exist in the provided text. ### Scenario Blueprint 3: The Automated Deployment (Agentic) **Objective:** Evaluate the "Plan-then-Execute" loop and the model's ability to self-correct. **Input:** A goal: "Deploy a multi-containerized microservice using Docker Compose, ensuring the database is initialized with a specific schema and the web service is accessible only on port 8080." **The Loop:** The model must output a `docker-compose.yml` and a `schema.sql`. The system will attempt to run these. If the `docker-compose.yml` fails (e.g., due to a port conflict or a missing volume), the error message is fed back to the model. **Success Criteria:** 1. The model successfully corrects the error in the second iteration. 2. The final configuration is functional. 3. The "Reasoning Trace" shows the model identifying the specific error message as the cause of the failure. **Failure Modes:** - **Looping:** The model repeatedly produces the same incorrect configuration. - **Reasoning Exhaustion:** The model "gives up" and provides a generic explanation of why it can't solve the problem after the second failure. - **Tool Misuse:** The model generates commands that are syntactically correct but logically impossible for the environment. # The Flight Recorder Data Schema To turn these tests into hard data, the Flight Recorder will output a structured JSON object for every request. This allows for automated post-processing and the generation of the charts mentioned earlier. ```json { "request_id": "uuid-v4", "timestamp": "ISO-8601", "model_metadata": { "model_name": "Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf", "quantization": "Balanced", "reasoning_budget": 8192, "context_window": 262144 }, "input_metrics": { "prompt_tokens": 4500, "context_fill_ratio": 0.017, "category": "Coding_Refactor" }, "inference_telemetry": { "ttft_ms": 1250, "tpot_ms": 45, "total_tokens_generated": 1200, "thinking_tokens": 3200, "final_answer_tokens": 880, "mtp_acceptance_rate": 0.88, "vulkan_memory_usage_mb": 28400 }, "reasoning_trace": [ {"token_index": 1, "content": "Thinking: I need to analyze the decorator pattern...", "type": "thought"}, {"token_index": 3200, "content": "Plan: 1. Define decorator. 2. Apply to functions.", "type": "thought"}, {"token_index": 3201, "content": "Here is the refactored code:", "type": "answer"} ], "outcome": { "pass_at_1": true, "error_log": null, "latency_score": "optimal" } } ``` By capturing `thinking_tokens` vs. `final_answer_tokens`, we can calculate the **Reasoning Efficiency Score (RES)**. This is a critical metric for the home-lab community because it helps users understand if the "Thinking" is actually productive or if the model is "hallucinating its own thoughts" (a known phenomenon where models generate long, coherent-sounding internal monologues that do not actually lead to a correct conclusion). # Memory Bandwidth and the R9700 Bottleneck In a 32GB-class R9700 server, the primary constraint for a 35B model is often not just the *capacity* of the memory, but the *bandwidth* of the memory bus. When running `llama.cpp` on Vulkan, the model weights must be moved from memory to the GPU's processing units for every single token generated (unless the entire model and KV cache fit perfectly into the fastest tier of VRAM). For a 35B model, the weights alone (depending on quantization) can occupy 18GB to 24GB. The math of inference speed is roughly: $Tokens\ Per\ Second \approx \frac{\text{Memory Bandwidth}}{\text{Model Weight Size}}$ If the R9700 uses DDR5 memory with a bandwidth of 60 GB/s, and the model weights occupy 20 GB, the theoretical maximum speed is 3 tokens per second. However, with MTP (Multi-Token Prediction) and optimized Vulkan kernels, we aim to exceed this by "batching" the weight movements. We will monitor the **Bandwidth Saturation** during the benchmark. If we see a sharp drop in TPS as the context window grows, it indicates that the KV cache is pushing the system into a state where memory bandwidth is over-subscribed. This data will be vital for helping users understand when to switch to a smaller model (e.g., a 14B model) to maintain a "snappy" user experience. # Vulkan-Specific Memory Pooling One of the more technical challenges we anticipate is how `llama.cpp` manages memory under the Vulkan backend when dealing with a 262k context window. Unlike CUDA, which has highly optimized memory management for deep learning, Vulkan requires more manual orchestration of memory pools. We will be testing two different memory allocation strategies: 1. **Static Allocation:** Pre-allocating the entire 262k context KV cache at startup. This ensures consistent performance but "locks" a massive amount of memory, potentially preventing other processes from running. 2. **Dynamic Allocation:** Growing the KV cache as the conversation progresses. This is more memory-efficient but can lead to "stuttering" (spikes in TPOT) as the system requests new memory blocks from the driver. The Flight Recorder will capture the `vulkan_memory_usage_mb` for every request. By comparing these two strategies, we can provide a recommendation for home-lab users: *Is it better to sacrifice RAM for consistent speed, or risk stuttering for a more flexible system?* # The "Lost in the Middle" Phenomenon A known issue with large context windows is that models tend to pay more attention to the beginning and the end of a prompt, often "forgetting" information buried in the middle. This is known as the "Lost in the Middle" phenomenon. To test this on the Qwen3.6-35B model, we will use a **Contextual Degradation Test**. We will place a specific fact (e.g., "The secret code is 9921") at different positions in a 100,000-token document: - At 1,000 tokens (Beginning) - At 50,000 tokens (Middle) - At 99,000 tokens (End) We will measure the model's accuracy at each position. If the accuracy drops significantly in the middle, it suggests that even with a large reasoning budget, the model's attention mechanism is struggling with the 262k window. This is a crucial finding for RAG applications, as it determines whether we need to implement more sophisticated "chunking" strategies or if we can rely on the model's raw context window. # MTP Drift and Correction Logic Multi-Token Prediction (MTP) is a powerful accelerator, but it is not perfect. Because the model is "guessing" future tokens, there is a risk of **MTP Drift**—where a small error in a draft token compounds, leading the model down a path of incoherence. In our benchmark, we will specifically look for "Correction Events." These are instances where the MTP head proposes a draft, but the primary model rejects it, forcing a re-calculation. - **High Acceptance / Low Quality:** The model accepts many draft tokens, but the final output is nonsensical. - **Low Acceptance / High Quality:** The model rejects most draft tokens, resulting in slow generation but high accuracy. - **The Sweet Spot:** The model accepts 70-80% of draft tokens and maintains high accuracy. By tracking the `mtp_acceptance_rate` in the Flight Recorder, we can determine if the "Balanced" quantization of the Qwen3.6-35B model is well-suited for MTP. If the acceptance rate is too low, it may indicate that the quantization is "smearing" the weights required for accurate draft prediction. # Expected Outcomes and Community Impact The ultimate goal of this technical draft is to move the home-lab community toward a more scientific approach to local AI. By documenting these specific metrics—Reasoning Density, MTP Acceptance, and Contextual Degradation—we provide a vocabulary for discussing local LLM performance that goes beyond "it feels fast." We expect to show that the R9700 server, when properly tuned with `llama.cpp`, Vulkan, and MTP, is capable of handling production-grade reasoning tasks. We want to prove that a 35B model with an 8,192 reasoning budget can handle complex coding migrations and multi-document RAG, provided the user understands the memory and bandwidth trade-offs involved. This data will be the foundation for a series of "Optimization Guides" for the community, helping them choose the right quantization, the right reasoning budget, and the right hardware configurations to achieve their specific goals—whether that is a private coding assistant, a massive-scale RAG system, or a high-fidelity creative writing partner. The data is coming. The recorder is running. The benchmark begins now.