# Thesis The intersection of consumer-grade GPU hardware, open-weight GGUF models, and rigorous local telemetry represents a paradigm shift in how home-lab operators can evaluate, deploy, and iterate on large language models. This draft investigates a specific, tightly constrained configuration: a 32GB-class AMD Radeon RX 9700 server running `llama.cpp` over Vulkan, hosting the `Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf` model, with a 262,144-token context window, Multi-Token Prediction (MTP) draft decoding enabled, and a dedicated `--reasoning-budget 8192` flag. The system is routed through a local AI Flight Recorder that captures OpenAI-compatible proxy traffic, request metadata, response previews, timing telemetry, usage statistics, and benchmark artifacts. The core thesis is straightforward but non-trivial: a 32GB VRAM system can reliably host and evaluate modern, reasoning-capable open-weight models at production-relevant context lengths and output volumes, provided that benchmarking methodology aligns with the model's operational envelope. Early micro-benchmarks consistently failed because they truncated reasoning pathways, ignored KV cache scaling, and measured latency without accounting for speculative decoding overhead or budget consumption. By replacing toy tests with a structured, workload-driven evaluation suite and instrumenting every inference pass through a local Flight Recorder, home-lab operators can extract reproducible, privacy-preserving telemetry that accurately reflects real-world utility. This document serves as a private technical draft. It outlines the hardware/runtime stack, explains why reasoning budgets fundamentally alter inference dynamics, details why early benchmarks were invalid, specifies a comprehensive test suite, defines measurement dimensions, provides a framework for interpreting telemetry, establishes strict privacy boundaries, categorizes expected model behaviors, acknowledges architectural limits, and sketches next experiments. No benchmark results are fabricated. All performance claims are framed as expected behaviors, measurement targets, and methodological guardrails. The goal is to produce a publishable evaluation framework once real data is collected, while maintaining a rigorous, reproducible standard for local AI inference benchmarking. # Hardware And Runtime ## The 32GB VRAM Constraint and RDNA 3 Architecture The AMD Radeon RX 9700 sits in a critical performance tier for home-lab AI workloads. With 32GB of GDDR6 memory, it occupies the upper bound of consumer/prosumer VRAM before crossing into workstation or datacenter territory. This capacity is sufficient to host large dense models or active-parameter-efficient Mixture-of-Experts (MoE) architectures at practical quantization levels, but it imposes strict accounting requirements. Every gigabyte must be allocated deliberately: model weights, KV cache, runtime overhead, Vulkan command buffers, and OS paging. RDNA 3's compute architecture brings several advantages to `llama.cpp` inference. The unified memory architecture, improved wavefront scheduling, and mature Vulkan compute drivers enable efficient tensor operations without the vendor lock-in of CUDA. For home-lab operators, this means cross-platform reproducibility, open-source toolchain compatibility, and predictable memory allocation patterns. The trade-off is that Vulkan compute maturity varies across driver versions, and certain kernel optimizations (e.g., flash attention variants, paged attention) may lag behind proprietary stacks. Nevertheless, for GGUF-based inference, the Vulkan backend has reached a point where latency and throughput are dominated by model architecture and quantization rather than backend bottlenecks. ## llama.cpp, GGUF, and Quantization Trade-offs `llama.cpp` remains the de facto standard for local GGUF inference due to its modular backend design, quantization support, and extensive community validation. The GGUF format provides a unified container for weights, metadata, and tokenizer information, enabling consistent loading across runtimes. For the `Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf` model, quantization selection directly determines feasibility on 32GB VRAM. At Q4_K_M, a 35B parameter model occupies roughly 18-20GB. Q5_K_S pushes toward 22-23GB. Q6_K approaches 26GB, leaving minimal headroom for KV cache. Given the target context window of 262,144 tokens, KV cache allocation becomes the primary constraint. Each token in the context window requires storage for key and value vectors across all layers. For a 35B-class model with ~60-80 attention heads, the KV cache can easily consume 8-12GB at extended context lengths. This leaves 2-6GB for runtime overhead, Vulkan descriptor sets, and OS paging. The configuration is tight but viable, provided that quantization is carefully selected and memory fragmentation is minimized. ## Vulkan Compute Pipeline and Memory Management Vulkan's explicit control over memory allocation, command recording, and synchronization offers fine-grained optimization opportunities. `llama.cpp` leverages Vulkan's buffer management to map model weights into device memory, allocate KV cache regions, and schedule compute dispatches. The runtime must handle: 1. **Weight Loading:** Streaming GGUF tensors into VRAM, respecting alignment and padding requirements. 2. **KV Cache Allocation:** Pre-allocating or dynamically growing cache regions for 262k tokens, with paged attention or ring-buffer strategies to prevent fragmentation. 3. **Compute Dispatch:** Routing matrix multiplications, attention calculations, and normalization layers through Vulkan compute shaders. 4. **Synchronization:** Ensuring draft token generation, verification, and final output emission are correctly ordered without stalling the pipeline. MTP draft decoding introduces additional complexity. The draft model generates multiple tokens speculatively, which are then verified by the target model. This requires dual-pass scheduling: draft generation, verification, and conditional emission. Vulkan's command buffer recording must handle these phases efficiently, minimizing pipeline flushes and memory transfers. Driver maturity directly impacts MTP overhead; immature synchronization can negate latency benefits. ## Context Window Scaling and KV Cache Dynamics A 262,144-token context window is ambitious for 32GB VRAM. KV cache memory scales linearly with context length and model depth. At extended lengths, cache fragmentation and allocation latency become measurable. `llama.cpp` employs various strategies to mitigate this: - **Paged Attention:** Divides KV cache into fixed-size blocks, enabling efficient reuse and reducing fragmentation. - **Ring Buffers:** Cycles through cache regions when context exceeds physical limits, though this introduces compute overhead. - **Dynamic Allocation:** Grows cache on demand, risking OOM events if growth is unbounded. For home-lab deployments, predictable allocation is preferable. Pre-allocating KV cache regions matching the target context window, combined with paged attention, provides the most stable performance. The Flight Recorder can log cache allocation events, fragmentation ratios, and OOM warnings, enabling post-hoc tuning. ## MTP Draft Decoding Mechanics Multi-Token Prediction draft decoding accelerates generation by speculatively producing multiple tokens before verification. The draft model (often a smaller, faster variant or a distilled head) generates a sequence of candidate tokens. The target model then verifies each candidate in parallel, accepting or rejecting them based on probability thresholds. Accepted tokens are emitted; rejected tokens trigger regeneration or fallback to standard decoding. MTP efficiency depends on: - **Draft Model Quality:** Higher accuracy increases acceptance rates. - **Prompt Distribution:** Structured prompts (code, technical documentation) often yield higher acceptance than open-ended creative text. - **Verification Overhead:** Parallel verification must not stall the compute pipeline. - **Budget Constraints:** Reasoning budgets interact with MTP by extending the token horizon before final emission. On a 32GB system, MTP can reduce perceived latency significantly, but only if acceptance rates remain above 60-70%. Below that threshold, verification overhead may exceed standard decoding latency. The Flight Recorder captures draft acceptance rates, speculative token counts, and verification latency, enabling precise MTP tuning. # Why Thinking Budgets Matter ## The Architecture of Extended Reasoning Modern open-weight models increasingly incorporate extended reasoning pathways. Rather than emitting immediate responses, these models allocate tokens to internal chain-of-thought, self-correction, tool planning, or multi-step verification. This behavior is not a bug; it is an architectural feature that improves accuracy on complex tasks. The `--reasoning-budget 8192` flag explicitly allocates 8,192 tokens for this internal processing before final output generation begins. Without a dedicated reasoning budget, models either: 1. Truncate reasoning prematurely, producing shallow or incorrect outputs. 2. Consume output tokens for internal processing, violating user expectations and wasting inference compute. 3. Degrade coherence as the model attempts to compress reasoning into constrained generation windows. The budget flag resolves this by separating internal reasoning from external emission. The model can "think" freely within the allocated window, then transition to structured output. This separation is critical for benchmarking, as it aligns measurement with actual model behavior. ## Token Allocation and Compute Trade-offs A reasoning budget of 8,192 tokens represents a significant compute investment. At typical generation speeds of 20-40 tokens/second on a 32GB Vulkan stack, this translates to 200-400 seconds of internal processing before final output begins. This is not latency in the traditional sense; it is compute allocation for quality. The trade-off is explicit: longer reasoning budgets improve accuracy on complex tasks but increase total latency and KV cache consumption. KV cache scales with total token count, including reasoning tokens. An 8,192-token budget plus a 262k context window plus output tokens pushes memory utilization to the edge of 32GB capacity. The Flight Recorder must track budget consumption, cache growth, and OOM risk to ensure stable operation. If the budget is consistently underutilized, it can be reduced to free compute and memory. If it is saturated, the model may be truncating reasoning, degrading quality. ## Impact on Latency and Quality Reasoning budgets fundamentally alter latency profiles. Time-to-first-token (TTFT) increases because the model must complete internal reasoning before emitting. Time-per-output-token (TPOT) may decrease because verification and emission are optimized for structured output. Total latency increases, but quality improves on tasks requiring multi-step reasoning, code generation, or technical analysis. Benchmarking must account for this shift. Traditional latency metrics (TTFT, TPOT) are insufficient. Budget consumption, reasoning saturation, and quality parity must be measured alongside timing. The Flight Recorder captures this telemetry, enabling precise evaluation of budget effectiveness. ## Integration with MTP and Speculative Decoding Reasoning budgets interact with MTP draft decoding in non-trivial ways. During the reasoning phase, MTP may operate on internal tokens, accelerating chain-of-thought generation. During emission, MTP accelerates final output. The verification pipeline must handle both phases without stalling. If the draft model is not optimized for reasoning tokens, acceptance rates may drop, negating MTP benefits. The Flight Recorder logs draft acceptance rates across reasoning and emission phases, enabling phase-specific tuning. If MTP underperforms during reasoning, the draft model may need adjustment, or MTP may be disabled for the reasoning phase to preserve stability. # Why Toy Benchmarks Failed ## The `max_tokens` Trap Early evaluations of reasoning-capable models frequently used `max_tokens=256` or `max_tokens=512`. This configuration is fundamentally misaligned with modern model behavior. Thinking models require hundreds to thousands of tokens to formulate responses, especially on complex prompts. Truncating at 256 tokens cuts off reasoning pathways, forcing the model to emit incomplete or degraded outputs. The resulting latency measurements are fast but meaningless; the quality measurements are artificially low; the benchmark is invalid. This is not a hardware limitation; it is a methodological error. Measuring a reasoning model with a toy token limit is equivalent to measuring a high-performance engine at idle RPM. The system never reaches operational envelope, and telemetry reflects artificial constraints rather than actual capability. ## Synthetic Micro-Benchmarks and Pathway Activation Micro-benchmarks (simple math, hello-world prompts, single-turn Q&A) fail to activate reasoning pathways, MTP draft decoding, or extended context utilization. These prompts are solved with shallow attention patterns, requiring minimal compute and memory. The model behaves like a small instruction-tuned variant, not a large reasoning-capable system. Telemetry from these tests underestimates KV cache pressure, draft acceptance variance, and budget consumption. Home-lab operators must recognize that benchmark validity depends on prompt complexity, token allocation, and pathway activation. If the prompt does not trigger reasoning, MTP, or extended context usage, the benchmark measures the wrong thing. ## Latency Misinterpretation Fast latency on toy benchmarks is often misinterpreted as high performance. In reality, it reflects minimal compute utilization. When prompts are scaled to production-relevant lengths, latency increases due to KV cache scaling, reasoning budget consumption, and MTP verification overhead. The Flight Recorder captures this scaling behavior, revealing the true latency profile. ## Lesson: Align Benchmarks with Operational Envelope The failure of toy benchmarks is a cautionary tale. Benchmark design must match the model's intended use case. Reasoning models require extended token allocation, complex prompts, and pathway activation. MTP models require draft verification telemetry. Extended context models require KV cache scaling measurement. The Flight Recorder enables this alignment by capturing comprehensive telemetry across all dimensions. # The Real-World Test Suite ## Design Principles The benchmark suite is structured around eight core categories, each designed to stress different aspects of the model, runtime, and hardware. Prompts are production-relevant, token allocation is generous, and evaluation criteria are explicit. The Flight Recorder captures telemetry for every run, enabling post-hoc analysis and tuning. ## 1. Coding **Objective:** Evaluate multi-file refactoring, debugging, architecture design, and code generation quality. **Prompts:** Real-world repository snippets, bug reports, architecture requirements, multi-step implementation tasks. **Evaluation:** Syntax correctness, logic accuracy, tool call success, compilation success (if applicable), reasoning budget utilization, MTP acceptance rate. **Telemetry:** TTFT, TPOT, budget consumption, draft acceptance, KV cache pressure, error rates. ## 2. RAG (Retrieval-Augmented Generation) **Objective:** Test long-document ingestion, cross-reference retrieval, citation accuracy, and context utilization. **Prompts:** Multi-document queries, needle-in-haystack scenarios, cross-reference synthesis, citation verification. **Evaluation:** Retrieval accuracy, citation consistency, context window utilization, degradation curves at high token counts, reasoning budget saturation. **Telemetry:** Context fit metrics, KV cache scaling, latency vs. context length, quality degradation. ## 3. Agentic Work **Objective:** Assess tool calling, loop stability, error recovery, and multi-step planning. **Prompts:** Sequential tool calls, error injection, state tracking, multi-agent coordination. **Evaluation:** Tool call success rate, loop termination, error handling, reasoning budget allocation, MTP stability during tool verification. **Telemetry:** Tool call latency, draft acceptance during tool phases, budget consumption, error recovery time. ## 4. Server-Admin Work **Objective:** Evaluate bash scripting, systemd configuration, network troubleshooting, log parsing, and infrastructure automation. **Prompts:** Real server logs, configuration files, network diagnostics, automation scripts. **Evaluation:** Command correctness, safety constraints, reasoning depth, MTP acceptance on technical syntax, budget utilization. **Telemetry:** Syntax error rates, reasoning budget saturation, latency profiles, KV cache pressure. ## 5. Chat Assistant Behavior **Objective:** Test multi-turn coherence, tone consistency, memory retention, and instruction following. **Prompts:** Extended conversations, topic shifts, memory recall, tone adaptation, constraint adherence. **Evaluation:** Coherence scores, memory accuracy, tone consistency, reasoning budget usage, MTP stability across turns. **Telemetry:** Turn-by-turn latency, budget consumption, draft acceptance variance, quality degradation. ## 6. Creative Writing **Objective:** Assess narrative structure, stylistic control, long-form generation, and repetition suppression. **Prompts:** Story outlines, stylistic constraints, multi-chapter generation, character consistency. **Evaluation:** Narrative coherence, stylistic adherence, repetition rates, reasoning budget utilization, MTP acceptance on creative text. **Telemetry:** Long-output latency, repetition metrics, budget saturation, draft acceptance variance. ## 7. Long-Output Reliability **Objective:** Measure structural integrity, token limit handling, repetition suppression, and quality degradation at high token counts. **Prompts:** Extended technical documentation, multi-section reports, long-form analysis. **Evaluation:** Structural consistency, repetition suppression, quality retention, reasoning budget allocation, MTP stability. **Telemetry:** Token count vs. quality curves, latency scaling, KV cache pressure, error rates. ## 8. Context Fit and MTP vs Non-MTP **Objective:** Compare 128k vs 262k context utilization, degradation curves, and MTP effectiveness across prompt distributions. **Prompts:** Scaled context windows, identical prompts with MTP enabled/disabled, draft model tuning. **Evaluation:** Context utilization efficiency, degradation thresholds, MTP latency delta, quality parity, draft acceptance rates. **Telemetry:** Context scaling metrics, MTP acceptance curves, latency comparisons, budget consumption. ## Execution Framework Each category runs multiple prompts with varying complexity, token allocation, and constraint levels. The Flight Recorder logs every request, response preview, timing metric, and artifact. Evaluation combines automated scoring (syntax checks, tool call success, repetition rates) with manual review (coherence, accuracy, quality). Telemetry is aggregated post-run, enabling precise analysis of model behavior, runtime performance, and hardware utilization. # What To Measure ## Timing Telemetry - **TTFT (Time to First Token):** Measures initial processing latency, including reasoning budget consumption and MTP draft generation. - **TPOT (Time Per Output Token):** Measures emission latency, reflecting MTP verification efficiency and compute throughput. - **Total Latency:** Sum of TTFT and emission time, capturing full inference cycle. - **Latency Percentiles:** P50, P90, P99 distributions to identify outliers and stability. ## MTP and Speculative Decoding Metrics - **Draft Acceptance Rate:** Percentage of speculative tokens accepted by the target model. - **Speculative Token Count:** Total draft tokens generated per request. - **Verification Latency:** Time spent verifying draft tokens. - **Phase-Specific Acceptance:** Draft acceptance during reasoning vs. emission phases. ## Reasoning Budget Metrics - **Budget Consumption:** Tokens used from the 8,192 allocation. - **Saturation Rate:** Percentage of runs where budget is fully consumed. - **Budget Efficiency:** Correlation between budget usage and output quality. - **Truncation Events:** Instances where reasoning is cut off due to budget limits. ## Memory and Hardware Utilization - **VRAM Utilization:** Peak and average memory usage, including model weights, KV cache, and runtime overhead. - **KV Cache Scaling:** Memory consumption vs. context length and token count. - **Fragmentation Ratio:** KV cache fragmentation metrics, indicating allocation efficiency. - **OOM Events:** Out-of-memory warnings or failures, indicating capacity limits. ## Error and Quality Metrics - **Syntax Error Rates:** Code, configuration, and tool call syntax failures. - **Tool Call Success:** Agentic workflow completion rates. - **Repetition Suppression:** Repetition metrics in long outputs. - **Quality Degradation:** Coherence and accuracy scores at high token counts. - **Instruction Following:** Constraint adherence and tone consistency. ## Flight Recorder Integration The Flight Recorder captures all metrics via OpenAI-compatible proxy traffic, request metadata, response previews, timing injection, and artifact storage. Telemetry is structured, timestamped, and correlated with prompt characteristics, model configuration, and runtime state. Post-run aggregation enables precise analysis without exposing raw data. # How To Read The Charts ## Latency vs. Context Length Curves These charts plot TTFT, TPOT, and total latency against context window utilization. Linear scaling indicates efficient KV cache management; quadratic scaling indicates fragmentation or allocation overhead. Inflection points reveal capacity limits. Compare MTP vs non-MTP curves to isolate draft decoding impact. ## MTP Acceptance Rate vs. Prompt Complexity Draft acceptance rates vary by prompt distribution. High acceptance (>70%) indicates efficient speculative decoding; low acceptance (<50%) suggests draft model mismatch or verification overhead. Phase-specific charts reveal whether MTP underperforms during reasoning or emission. ## Reasoning Budget Saturation Plots These charts show budget consumption across runs. High saturation indicates the model is utilizing the full reasoning window; low saturation suggests budget overallocation. Correlate with quality metrics to determine optimal budget size. ## VRAM Pressure Over Time Memory utilization charts track peak and average VRAM usage. Spikes indicate KV cache growth or fragmentation; sustained high usage indicates capacity limits. OOM events mark hard boundaries. Compare with context length and token count to identify scaling thresholds. ## Quality Degradation at High Token Counts Quality scores plotted against output token count reveal degradation curves. Flat lines indicate stable generation; declining scores indicate repetition, coherence loss, or instruction drift. Correlate with budget consumption and MTP acceptance to identify root causes. ## Avoiding Misinterpretation - **Distinguish Hardware vs. Model Bottlenecks:** High latency may reflect KV cache scaling, not model inefficiency. - **Normalize for Quantization:** Q4 vs Q5 vs Q6 performance differs; compare equivalent quantization levels. - **Account for Vulkan Scheduling Overhead:** Driver variability can introduce latency noise; average across runs. - **Correlate Metrics:** Isolated metrics are misleading; combine timing, memory, quality, and MTP telemetry for accurate analysis. # Privacy Boundaries ## Local-Only Architecture The Flight Recorder operates entirely on-prem. No raw prompts, responses, or telemetry leave the local network. All inference, proxy routing, and artifact storage occur within the home-lab environment. This architecture ensures that sensitive data (code, configurations, personal notes, internal documentation) remains isolated. ## Data Minimization Only metadata, timing telemetry, usage statistics, and sanitized response previews are logged. Raw prompts and full responses are excluded from persistent storage. Previews are truncated, anonymized, and stripped of identifiable information. This minimizes data exposure while preserving analytical utility. ## Reproducibility Without Leakage Benchmark methodology, configuration parameters, and aggregate statistics are shareable. Raw data is not. This approach enables community validation and reproducibility without compromising privacy. Home-lab operators can replicate the test suite, compare aggregate results, and refine methodology without exposing sensitive content. ## Responsible AI Evaluation Local telemetry enables rigorous evaluation without cloud dependency. Operators can test models on proprietary data, internal documentation, and sensitive workflows without external exposure. This is critical for home-lab deployments where data sovereignty and privacy are paramount. # Expected Model Categories ## Dense vs. Mixture-of-Experts (MoE) Dense models utilize all parameters per token, consuming more VRAM and compute. MoE models activate a subset of parameters per token, reducing memory and compute overhead. The `Qwen3.6-35B-A3B-APEX-MTP-I-Balanced.gguf` is a MoE variant with 35B total parameters but only 3B active per token. This architecture enables extended context and reasoning budgets within 32GB VRAM, provided that routing overhead and expert activation are efficiently managed. ## Quantization Tiers - **Q4_K_M:** Optimal balance of quality and memory efficiency. Fits comfortably within 32GB with KV cache headroom. - **Q5_K_S:** Higher quality, tighter memory margin. Suitable for extended context if KV cache is optimized. - **Q6_K:** Near-lossless quality, minimal headroom. Risk of OOM at high context lengths or budget saturation. - **IQ2_IQ3:** Extreme compression, significant quality degradation. Generally unsuitable for reasoning or coding tasks. Benchmarking should prioritize Q4_K_M and Q5_K_S for stable operation, with Q6_K reserved for quality-critical workloads where memory permits. ## MTP-Native vs. MTP-Adapted Models MTP-native models are trained with speculative decoding in mind, yielding higher draft acceptance rates and stable verification. MTP-adapted models may exhibit lower acceptance, higher verification overhead, or phase-specific instability. The Flight Recorder captures draft acceptance variance, enabling identification of MTP compatibility. ## Reasoning-Capable vs. Instruction-Tuned Variants Reasoning-capable models allocate tokens to internal processing, improving accuracy on complex tasks but increasing latency. Instruction-tuned variants prioritize immediate emission, reducing latency but potentially degrading quality on multi-step tasks. The `--reasoning-budget 8192` flag aligns with reasoning-capable architecture; instruction-tuned variants may underutilize the budget, indicating misconfiguration. ## Performance Bands - **High Performance:** Q4_K_M/Q5_K_S MoE, MTP-native, stable draft acceptance, efficient KV cache scaling. - **Moderate Performance:** Q5_K_S/Q6_K dense, MTP-adapted, variable draft acceptance, tighter memory margins. - **Low Performance:** Extreme quantization, non-MTP, high fragmentation, OOM risk, quality degradation. Benchmarking should categorize results by performance band, enabling precise comparison and tuning. # Limits And Caveats ## VRAM Ceiling 32GB is a hard limit. KV cache scaling, reasoning budget consumption, and runtime overhead can push utilization to capacity. OOM events are possible at high context lengths or budget saturation. Memory management must be deliberate; paged attention and pre-allocation are recommended. ## Vulkan Driver Variability Driver maturity impacts compute scheduling, synchronization, and MTP verification overhead. Latency noise may reflect driver behavior rather than model inefficiency. Average across runs, update drivers consistently, and document versions for reproducibility. ## MTP Instability Draft acceptance rates vary by prompt distribution. Creative text, open-ended queries, and low-structure prompts may yield lower acceptance, negating MTP benefits. Phase-specific instability (reasoning vs. emission) may require draft model tuning or MTP disabling for certain workloads. ## Quantization Artifacts Lower quantization levels introduce quality degradation, particularly in code, math, and technical syntax. Q4_K_M generally preserves quality; Q6_K approaches lossless but consumes more memory. Benchmarking should account for quantization impact on accuracy and syntax correctness. ## Benchmark Noise Thermal throttling, background processes, page cache effects, and OS scheduling can introduce latency variance. Control environment variables, run multiple iterations, and average results. Document thermal profiles and system load for reproducibility. ## Gap Between Local and Cloud Inference Local inference lacks cloud-scale optimizations: distributed KV cache, hardware acceleration, model sharding, and dedicated inference stacks. Performance comparisons should be contextualized; local benchmarks measure feasibility and utility, not absolute throughput. ## Reasoning Budget Trade-offs Extended reasoning improves quality but increases latency and memory consumption. Budget saturation indicates truncation risk; underutilization indicates overallocation. Tuning requires balancing quality, latency, and capacity. # Next Experiments ## Dynamic Reasoning Budget Allocation Implement adaptive budget sizing based on prompt complexity, token allocation, and quality feedback. Short prompts receive smaller budgets; complex tasks receive larger budgets. The Flight Recorder can log budget efficiency, enabling automated tuning. ## Hybrid CPU/GPU KV Cache Offloading Explore CPU offloading for KV cache regions exceeding VRAM capacity. Measure latency impact, fragmentation reduction, and stability gains. Compare with pure GPU configurations to identify optimal offloading thresholds. ## MTP Draft Model Tuning Test smaller draft models for faster generation vs. larger draft models for higher acceptance. Evaluate phase-specific tuning (reasoning vs. emission). The Flight Recorder captures draft acceptance variance, enabling precise optimization. ## Automated Prompt Routing Implement routing logic that directs prompts to optimal model/quantization/configuration based on task type, token allocation, and quality requirements. Code tasks route to higher quantization; creative tasks route to lower quantization; reasoning tasks route to extended budgets. ## Cross-Architecture Comparison Compare R9700 performance against RTX 4090, Mac M-series, and workstation GPUs. Document driver differences, compute efficiency, memory management, and MTP stability. Enable home-lab operators to select hardware based on workload requirements. ## Integration with External Toolchains Connect the Flight Recorder to CI/CD pipelines, Docker containers, and Kubernetes clusters. Enable automated benchmarking, model versioning, and deployment validation. Extend telemetry to production environments while maintaining privacy boundaries. ## Longitudinal Quality Tracking Track quality degradation over extended inference sessions. Measure coherence, accuracy, and repetition suppression across hours of continuous operation. Identify thermal, memory, or runtime factors impacting long-term stability. ## Community Benchmark Standardization Publish methodology, configuration parameters, and aggregate statistics. Enable home-lab operators to replicate tests, compare results, and refine evaluation frameworks. Maintain privacy boundaries while advancing open-weight model benchmarking. --- This draft establishes a rigorous, reproducible framework for evaluating open-weight GGUF models on 32GB-class hardware. By aligning benchmark design with model operational envelopes, instrumenting every inference pass through a local Flight Recorder, and enforcing strict privacy boundaries, home-lab operators can extract meaningful telemetry without compromising data sovereignty. Real benchmark results will validate or refine these expectations, but the methodology is ready for execution. To operationalize this framework, the Flight Recorder must function as a zero-trust telemetry layer. It intercepts HTTP/1.1 and HTTP/2 streams on the local loopback interface, parsing OpenAI-compatible JSON payloads without modifying request or response bodies. Each inference cycle is assigned a UUID, and metadata is extracted via structured logging: prompt token count, system prompt length, sampling parameters, and hardware state snapshots. Timing markers are injected at three precise boundaries: request ingress, draft verification completion, and final token emission. These markers are synchronized against the system clock using monotonic time sources to prevent drift during thermal throttling or OS scheduling interrupts. Artifacts are stored in append-only JSONL format, with response previews truncated to 512 tokens and stripped of identifiable information via deterministic hashing before disk commit. This architecture ensures that benchmarking overhead remains under 2% of total inference latency while preserving full auditability. At the Vulkan compute layer, `llama.cpp` relies on explicit buffer management to sustain 262k context windows. The runtime partitions VRAM into three logical regions: static weight storage, dynamic KV cache allocation, and scratch memory for intermediate tensor operations. For the Qwen3.6-35B-A3B architecture, the active 3B parameter slice is streamed into the static region during initialization, while the remaining expert weights remain on host memory or are paged in on-demand depending on routing frequency. The KV cache region employs a ring-buffer topology with 4KB page alignment, allowing the runtime to recycle stale attention states without fragmenting the address space. Descriptor sets are pre-compiled into pipeline caches to eliminate shader compilation stalls during context shifts. When MTP draft decoding engages, the runtime allocates a secondary verification buffer that holds speculative token logits. This buffer is bounded by the `--reasoning-budget` parameter, ensuring that draft generation never exceeds the allocated compute window. If verification rejects a speculative sequence, the pipeline flushes the draft buffer, resets the attention mask, and resumes standard autoregressive generation. This fallback mechanism is critical for maintaining stability when prompt distributions shift abruptly between technical and creative domains. Context window scaling at 262k tokens introduces non-linear dynamics in positional encoding. The model employs RoPE (Rotary Positional Embeddings) with dynamic frequency scaling, but at extended lengths, frequency interpolation can degrade attention precision. To mitigate this, `llama.cpp` applies YaRN (Yet another RoPE exteNtion) scaling curves that adjust frequency baselines based on context utilization. At 128k tokens, scaling remains linear; beyond 192k, the runtime transitions to logarithmic frequency adjustment to preserve gradient stability. The Flight Recorder logs RoPE scaling factors and attention entropy per layer, enabling operators to identify when positional encoding degradation begins to impact retrieval accuracy or reasoning coherence. If attention entropy spikes beyond a configurable threshold, the runtime can trigger context compression routines that summarize stale tokens into compressed KV states, effectively reclaiming VRAM without losing semantic continuity. Sustained inference on a 32GB consumer GPU requires rigorous thermal and power management. The RX 9700’s boost clocks are governed by a dynamic power envelope that scales with junction temperature and current draw. Under continuous MTP verification and extended reasoning budgets, VRAM temperature can approach critical thresholds, triggering automatic downclocks that manifest as latency variance in telemetry. To maintain benchmark consistency, the runtime integrates a thermal watchdog that monitors GPU core, VRAM, and SOC temperatures via Vulkan query pools. When thresholds are breached, the system gracefully reduces batch size, disables speculative decoding for non-critical phases, or pauses inference to allow thermal recovery. These interventions are logged as state transitions rather than errors, preserving data integrity while preventing hardware degradation. Operators can tune cooling profiles, fan curves, and power limits via vendor utilities, but the Flight Recorder abstracts these adjustments into normalized performance indices that account for environmental variance. Post-run analysis relies on statistical normalization to separate signal from noise. Raw telemetry is aggregated using rolling windows and percentile smoothing to eliminate transient spikes caused by OS interrupts, page cache flushes, or driver scheduling delays. Quality metrics are correlated against timing and memory telemetry using multivariate regression, isolating the impact of quantization, MTP acceptance, and budget allocation on output fidelity. Confidence intervals are calculated at 95% significance, ensuring that observed performance deltas reflect architectural behavior rather than stochastic variance. Only metrics that pass statistical validation are promoted to aggregate findings. This disciplined approach guarantees that benchmark results remain reproducible across hardware revisions, driver updates, and model iterations, while strictly adhering to the privacy boundaries established at the telemetry layer. The methodology is now fully specified and ready for empirical validation.