Optimizing Qwen3.6 / Qwen3.8-27B on 16GB VRAM: Complete Benchmark Results and Setup Guide (~30-50tps at 32k to 72k context)

This post was made with AI. I tried to remove as much slop as possible and keep it straight to the point to save your time as I know how annoying AI slop posts can be, but I still wanted to retain all the details so it can be used as a resource for comparison with other future quants, I advise that any humans just skim through it or read the 1st section and run the balanced profile with a context smart harness like pi or deepseek harness (apparently really good for this) Optimizing Qwen3.6 / Qwen3.8 27B on 16GB VRAM: Complete Experimental Log, Benchmarks, and Deployment Guide This document records the complete set of benchmarks, quantization evaluations, KV cache sweeps, speculative decoding experiments, and context scaling tests conducted across multiple GPU architectures to determine the optimal configuration for running Qwen 27B hybrid models within a 16GB VRAM constraint. 1. Primary Recommendations (Quick Reference) 1.1 Balanced Profile (Recommended Default) Model: Qwen3.8-27B-IQ4_XS-pure-MTP.gguf (14.56 GB) Inference Engine: beellama.cpp KV Cache Format: kvarn4 (4-bit variance-normalized KVarN) Speculative Decoding: Native MTP at draft depth 2 (--spec-type draft-mtp --spec-draft-n-max 2 --spec-draft-p-min 0.0) Maximum Context (100% VRAM): 32,768 to 48,000 tokens Quality Metric: 92.55% Top-1 token match, 0.1169 mean KL divergence vs. uncompressed Q8_0 reference 1.2 Extended Context Profile (For Context > 48K) Model: Qwen3.8-27B-AD-IQ3_S-IQ3_XXS.gguf (12.98 GB) KV Cache Format: kvarn4 Speculative Decoding: Native MTP at draft depth 2 Maximum Context (100% VRAM): ~72,000 tokens (+50% context headroom) Quality Metric: 89.85% Top-1 token match, 0.2282 mean KL divergence vs. uncompressed Q8_0 reference 1.3 Recommended Server Launch Command export LD_LIBRARY_PATH=/usr/local/cuda/lib64:LD_LIBRARY_PATH ./build/bin/llama-server -m ./models/Qwen3.8-27B-IQ4_XS-pure-MTP.gguf --spec-type draft-mtp --spec-draft-n-max 2 --spec-draft-p-min 0.0 --cache-type-k kvarn4 --cache-type-v kvarn4 -c 32768 -ngl 999 -fa on -np 1 --fit off -t 8 --host 127.0.0.1 --port 8080 --api-key your-key-here 2. Quantization Quality Benchmarks 2.1 Final Evaluation vs. Q8_0 Gold Reference Evaluation conducted using llama-perplexity against a gold reference generated from uncompressed Qwen3.8-27B-Q8_0.gguf (29.05 GB, PPL 3.9616). Corpus: 20 randomly sampled reasoning and math problems from canada-quant/hy3-w4a16-mtp-calibration (51,831 bytes, 24 chunks of 512 tokens). KV Cache: kvarn3 throughout to maintain exact numerical consistency. Logits Reference: Published on HuggingFace Hub as MaxDevv/Qwen3.8-27b-Q8-Logits-for-kld (3.04 GB). Model Quantization File Size PPL (hy3, 512 ctx) Mean KLD vs. Q8 (Lower = Better) Top-1 Match vs. Q8 (Higher = Better) RMS Δp Notes Q8_0 (Gold Baseline) 29.05 GB 3.9616 ± 0.131 0.0000 100.0% — Uncompressed reference baseline Qwen3.8 IQ4_XS-pure 14.56 GB 4.1474 ± 0.142 0.1169 ± 0.011 92.55 ± 0.34% 8.87% Highest distribution fidelity; recommended Atomic AD-IQ3_S-IQ3_XXS 12.98 GB 3.9594 ± 0.129* 0.2282 ± 0.014 89.85 ± 0.39% 12.87% Best sub-4-bit quant; recommended for high context Atomic AD-IQ3_XXS 12.08 GB 4.8501 ± 0.183 0.3100 ± 0.018 87.93 ± 0.42% 13.91% Plain 3-bit dynamic; moderate degradation Ridge 3.7bpw 12.60 GB 4.5645 ± 0.163 0.3554 ± 0.019 86.44 ± 0.44% 15.16% Fixed 3.7bpw; notable distribution divergence Qwen3.6 IQ3_XXS-MTP 12.20 GB 4.6392 ± 0.185 0.9034 ± 0.035 78.79 ± 0.52% 22.99% Evaluated on Qwen3.6 weights; severe quality drop Note on Atomic PPL: The* hy3 dataset serves as the calibration source for the Atomic/w4a16 quantization pipeline. This artificially flatters its raw PPL score on this corpus. The KLD (0.2282) and Top-1 match (89.85%) reflect its true, unbiased generalization accuracy. 2.2 Preliminary Wikitext-2 Quality Benchmarks Earlier exploratory testing evaluated quants against a 4-bit IQ4_XS-pure local reference over 20 chunks of Wikitext-2 (512 context): Model Quantization File Size Perplexity (Wikitext-2) Mean KLD vs. 4-bit Ref Evaluation Qwen3.8 IQ4_XS-pure 14.56 GB 7.1252 ± 0.249 0.0000 (Ref) Baseline 4-bit reference Atomic AD-IQ3_S-IQ3_XXS 12.98 GB 7.3215 ± 0.256 0.0567 Retains high linguistic consistency Ridge 3.7bpw 12.60 GB 7.6710 ± 0.267 0.1373 +7.7% PPL degradation vs. 4-bit Qwen3.6 IQ3_XXS-MTP 12.20 GB 7.8892 ± 0.296 0.3552 Demonstrates clear sub-3.5bpw degradation 2.3 External Baseline Reference: Comprehensive Evaluation of Qwen3.6-27B Quantizations To contextualize experimental results against baselines, the table below compiles the empirical benchmark dataset published by Quesma (Piotr Migdał, Do Qwen3.6 27B quantizations break the pelican?, July 2026). This dataset tracks statistical distribution drift, downstream mathematical reasoning, terminal agent execution, generative SVG visual fidelity, and M5 Max execution throughput across 24 distinct quantization formats derived from Qwen3.6-27B-BF16. Master Quantization Reference Table Quantization Format Family Size (GB) Mean KLD vs. BF16 Top-1 Match (Temp 0) Token Collision (Temp 1) Wikitext-2 PPL AIME-120 Solved (%) Terminal-Bench 2.1 Passed (%) Pelican Bradley-Terry Score Generation Speed (M5 Max) BF16 (Reference) Uncompressed 54.7 0.0000 100.0% 48.48% 6.40 70.8% (85/120) 38.2% (34/89) +1.76 ± 9.05 24.9 tok/s UD-Q8_K_XL Unsloth Dynamic 35.8 0.00038 99.4% 48.47% 6.39 70.0% (p=1.00) — +1.27 ± 3.53 31.8 tok/s FP8 vLLM Float 30.9 0.01700 96.7% — — — — -0.21 ± 1.90 — Q8_0 Standard K 29.0 0.00049 99.3% 48.46% 6.39 67.5% (p=0.45) 36.0% (p=0.75) +1.19 ± 2.09 30.8 tok/s UD-Q6_K_XL Unsloth Dynamic 26.0 0.00140 98.8% 48.44% 6.40 65.8% (p=0.18) — +1.70 ± 1.07 33.1 tok/s NVFP4 (Unsloth) vLLM Float 23.3 0.04400 92.2% — — — — +0.27 ± 0.81 — Q6_K Standard K 22.9 0.00210 98.3% 48.46% 6.41 64.2% (p=0.12) 30.3% (p=0.17) +2.54 ± 2.59 33.0 tok/s NVFP4 (NVIDIA) vLLM Float 21.9 0.03900 92.1% — — — — +1.77 ± 2.63 — UD-Q5_K_XL Unsloth Dynamic 20.4 0.00460 97.4% 48.41% 6.40 70.8% (p=1.00) — +1.39 ± 1.02 30.3 tok/s Q5_K_M Standard K 19.8 0.00520 97.2% 48.38% 6.40 65.0% (p=0.12) 29.2% (p=0.13) +1.12 ± 0.88 29.5 tok/s Q5_K_S Standard K 19.3 0.00580 97.1% 48.39% 6.40 67.5% (p=0.52) — +1.59 ± 0.80 30.4 tok/s UD-Q4_K_XL Unsloth Dynamic 17.9 0.01300 95.8% 48.29% 6.45 70.8% (p=1.00) — +0.48 ± 0.85 32.8 tok/s Q4_1 Legacy Q4 17.5 0.01800 94.8% 48.19% 6.43 64.2% (p=0.14) — +0.82 ± 4.09 34.4 tok/s Q4_K_M Standard K 17.1 0.01700 95.1% 48.29% 6.45 65.8% (p=0.21) 40.4% (p=0.79) +1.09 ± 0.90 32.7 tok/s IQ4_NL Importance Matrix 16.3 0.01800 94.8% 48.19% 6.45 66.7% (p=0.33) — +2.54 ± 6.36 32.7 tok/s Q4_0 Legacy Q4 16.1 0.03500 93.0% 48.10% 6.55 64.2% (p=0.15) — +1.31 ± 0.65 34.7 tok/s Q4_K_S Standard K 16.1 0.01900 94.7% 48.23% 6.46 63.3% (p=0.06) — +2.18 ± 0.98 33.5 tok/s IQ4_XS Importance Matrix 15.7 0.01800 94.7% 48.20% 6.48 66.7% (p=0.27) — +2.32 ± 1.80 27.4 tok/s UD-Q3_K_XL Unsloth Dynamic 14.8 0.03800 92.5% 48.16% 6.51 72.5% (p=0.81) 31.5% (p=0.24) +0.46 ± 1.87 28.7 tok/s Q3_K_M Standard K 13.8 0.05000 91.0% 48.10% 6.50 73.3% (p=0.65) — +0.80 ± 0.76 32.1 tok/s Q3_K_S Standard K 12.6 0.08300 88.3% 47.49% 6.71 54.2% (p<0.001) — +0.99 ± 1.88 31.3 tok/s UD-IQ3_XXS Unsloth Dynamic 12.2 0.08600 88.2% 47.02% 6.82 52.5% (p<0.001) — -0.32 ± 2.66 26.0 tok/s UD-Q2_K_XL Unsloth Dynamic 12.0 0.12000 86.5% 45.73% 6.84 56.7% (p<0.001) 32.6% (p=0.30) -1.25 ± 2.50 30.9 tok/s UD-IQ2_M Unsloth Dynamic 11.0 0.13000 85.5% 45.82% 7.03 54.2% (p<0.001) — -0.61 ± 2.13 26.6 tok/s UD-IQ2_XXS Unsloth Dynamic 9.6 0.28000 78.7% 43.53% 8.19 32.5% (p<0.001) — -3.29 ± 1.95 27.8 tok/s Metric Definitions and Key Insights Statistical Collapse Threshold (ext{KLD} le 0.050 vs. ext{KLD} > 0.080): Quantizations maintaining ext{KLD} le 0.050 (Q4_K_M, UD-Q4_K_XL, Q3_K_M, UD-Q3_K_XL) exhibit no statistically significant degradation on complex reasoning (AIME-120 accuracy matches BF16 within standard error margins). Once ext{KLD} exceeds 0.080 (Q3_K_S, UD-IQ3_XXS, and all 2-bit formats), reasoning performance drops sharply (p < 0.001). On AIME-120, accuracy drops from ~70–73% down to 32–56%. Top-1 Agreement (Greedy Decoding Fidelity): Measures token-level exact match against the unquantized BF16 model at T = 0. Q8_0 reaches 99.3%, 4-bit quants achieve 93.0%–95.8%, and 3-bit quants achieve 88.2%–92.5%. Below 3-bit, agreement degrades to 78.7% (UD-IQ2_XXS), causing severe output drift. Collision Cross-Entropy (Sampling Consistency at T = 1.0): Measures the probability that independent random samples from the quantized model collide with samples from BF16. The unquantized baseline BF16 compared against itself has an inherent collision ceiling of 48.48%. Standard 8-bit to 4-bit models stay tightly clustered between 48.10% and 48.47%. Degradation only becomes pronounced below 3 bits (dropping to 43.53% on UD-IQ2_XXS). Visual Spatial Reasoning (Pelican Bradley-Terry Elo): Assessed via blind pairwise generation duels of complex vector graphics ("a pelican riding a bicycle" and multi-gear train animation scripts) judged by Gemini 3.6 Flash. Models ge ext{4-bit} show score parity dominated by generation seed variance. 2-bit models (UD-IQ2_XXS at -3.29) fail consistently due to SVG syntax errors, malformed coordinate paths, and dropped rendering tags. 2.4 Technical Note on IQ4_XS-pure Standard llama.cpp builds hardcode attention projection tensors (attn_qkv) to Q5_K (via commit 1dab5f5a), inflating file size from 14.6 GB to 15.1 GB without measurable quality benefit. Standard IQ4_XS: 15.1 GB (Exceeds safe limits at 32K context on 16GB cards). IQ4_XS-pure: 14.56 GB (Identical PPL: 7.3804 vs 7.3765, frees ~550 MB VRAM). 3. KV Cache Compression Benchmarks Tested on an RTX 5060 Ti 16GB with single-stream decode at 32,768 fully-filled context tokens using Qwen3.6/3.8 IQ4_XS-pure with MTP draft depth 2: KV Cache Type Implementation Bits / Value Compression Decode Speed (@32K filled) PPL vs. FP16 (6.5407) Quality Impact f16 Reference 16.0 1.0× — (OOM at 32K) 6.5407 Full precision turbo2 TheTom 2.25 6.4× 40.86 tok/s KLD 0.0056 Noticeable distribution drift kvarn5 beellama 5.0 3.2× 38.33 tok/s ~Lossless Minor throughput improvement kvarn4 beellama 4.0 4.0× 38.10 tok/s 6.5523 (+0.2%) Optimal throughput and quality kvarn3 beellama 3.0 5.3× 36.71 tok/s 6.5415 (= FP16) Mathematically lossless turbo4 TheTom 4.25 3.8× 34.69 tok/s ~Lossless Slower decode execution turbo3 TheTom 3.25 4.9× 33.76 tok/s ~Lossless Slower decode execution q8_0 Stock llama.cpp 8.0 2.0× 21.30 tok/s ~Lossless High memory bandwidth overhead 4. Speculative Decoding Sweep: MTP vs. dflash vs. ngram 4.1 Multi-Token Prediction (MTP) Tuning MTP uses the built-in draft prediction head embedded in the base model GGUF. Tested with turbo3 KV cache at 32,768 filled context: Draft Length (n-max) Minimum Probability (p-min) Decode Speed (@32K filled) Acceptance Ratio Notes n = 1 p = 0.0 16.40 tok/s 1.00 Autoregressive baseline equivalent n = 2 p = 0.0 22.10 tok/s 0.79 Optimal balance of speed and stability n = 3 p = 0.0 13.60 tok/s 0.52 Rejection penalty degrades throughput n = 4 p = 0.0 21.40 tok/s 0.68 High variance across token sequences n = 4 p = 0.5 6.40 tok/s 0.21 Early truncation causes execution stalls 4.2 dflash (Block Diffusion) Speculative Decoding Evaluated using standalone diffusion draft models (dflash-Qwen3.6-27B) across various draft quantizations and draft depths: Target Quant Draft Model Quant Draft Length (n) Target Hardware Context Length Decode Speed Evaluation UD-IQ3_XXS dflash-Q4_0 (0.98 GB) n = 4 4060 Ti 512 / 512 50.06 tok/s Fast, draft fits comfortably UD-IQ3_XXS dflash-Q4_0 (0.98 GB) n = 8 4060 Ti 512 / 512 43.90 tok/s Diminishing acceptance at n=8 UD-IQ3_XXS dflash-Q8_0 (1.85 GB) n = 4 4060 Ti 512 / 512 54.85 tok/s Highest peak speed; draft tight on VRAM UD-IQ3_XXS dflash-Q8_0 (1.85 GB) n = 8 4060 Ti 512 / 512 OOM CUDA compute buffer exceeds 16GB UD-IQ3_XXS dflash-Q4_0 (0.98 GB) n = 8 T4 (16GB) 512 / 512 24.20 tok/s Max speed achieved on T4 Q3_K_S dflash-Q4_0 (0.98 GB) n = 4 T4 (16GB) 512 / 512 18.64 tok/s Limited by compute bounds UD-IQ3_XXS dflash-Q4_0 (0.98 GB) n = 8 5060 Ti 16K Cap 31.42 tok/s Low acceptance (0.35) at 16K Comparison Summary: While dflash achieves high peak throughput on short contexts with small target models (up to 54.8 tok/s), it requires 1.0–1.85 GB of dedicated VRAM for the draft model. Native MTP requires only ~486 MB of draft context, delivers consistent acceptance rates at long contexts, and allows larger base models (IQ4_XS) to fit on 16GB hardware. 4.3 Draft-less n-gram Speculative Decoding (ngram-mod) Flags: --spec-type ngram-mod --spec-ngram-mod-n-min 16 --spec-ngram-mod-n-max 32 --spec-ngram-mod-n-match 24 VRAM Overhead: 0 MiB GPU memory. Performance: Provides large throughput acceleration (+35 to +135 tok/s) when generating repetitive structures or editing text present in context (e.g., refactoring code). Yields zero benefit on novel generation. 5. Multi-System Cross-Hardware Benchmarks Single-stream decode performance (batch size 1, 512 prompt tokens / 512 generated tokens) across tested hardware platforms: Hardware Platform Architecture Compute Cap Memory Bandwidth Quantization Model Speculative Configuration Decode Throughput Modal Tesla T4 Turing sm_75 320 GB/s bartowski Q3_K_M None 8.70 tok/s Modal Tesla T4 Turing sm_75 320 GB/s unsloth Q3_K_M-MTP None 10.29 tok/s Modal Tesla T4 Turing sm_75 320 GB/s unsloth UD-Q3_K_XL None 10.33 tok/s Modal Tesla T4 Turing sm_75 320 GB/s Q3_K_M-MTP MTP n = 2 16.12 tok/s Modal Tesla T4 Turing sm_75 320 GB/s UD-IQ3_XXS dflash-Q4 (n = 8) 24.20 tok/s RTX 4060 Ti 16GB Ada sm_89 288 GB/s Q3_K_M-MTP None 19.12 tok/s RTX 4060 Ti 16GB Ada sm_89 288 GB/s Q3_K_M-MTP MTP n = 2 34.31 tok/s RTX 4060 Ti 16GB Ada sm_89 288 GB/s UD-IQ3_XXS dflash-Q8 (n = 4) 54.85 tok/s RTX 5060 Ti 16GB Blackwell sm_120 448 GB/s Q3_K_M-MTP None 26.85 tok/s RTX 5060 Ti 16GB Blackwell sm_120 448 GB/s Q3_K_M-MTP MTP n = 2 46.94 tok/s RTX 5060 Ti 16GB Blackwell sm_120 448 GB/s IQ4_XS-pure-MTP MTP n = 2 + turbo3 54.31 tok/s RTX 5060 Ti 16GB Blackwell sm_120 448 GB/s IQ4_XS-pure-MTP MTP n = 2 + kvarn4 (@32K) 38.10 tok/s Turing Hardware Bottleneck Analysis The Tesla T4 (sm_75) lacks native hardware acceleration for BF16 tensor operations. The 48 Gated DeltaNet recurrent scan layers execute sequentially in FP32, preventing batched speculative verification from scaling effectively. As a result, T4 throughput is physically bounded to ~25–33 tok/s regardless of quantization level. 6. Context Scaling and Memory Ceilings on 16GB VRAM Memory utilization models on a 16,384 MiB physical VRAM budget with kvarn4 KV cache quantization: Model Quantization Model Weight VRAM Draft Context VRAM Maximum Usable Context (100% GPU) Decode Speed at Max Context Operating Status IQ4_XS-pure-MTP 13.56 GiB 486 MiB ~48,000 tokens ~37.4 tok/s (5060 Ti) Primary recommended profile Atomic AD-IQ3_S 12.09 GiB 486 MiB ~72,000 tokens ~36.0 tok/s (5060 Ti) High-context profile IQ3_XXS-MTP 11.17 GiB 486 MiB ~88,000 tokens 35.19 tok/s (5060 Ti) Extreme context; high quality degradation IQ4_XS-pure (No MTP) 13.56 GiB 0 MiB ~80,000 tokens 18.64 tok/s (5060 Ti) Stable non-speculative baseline IQ4_XS-pure (FFN Offload -ot) 8.20 GiB GPU 0 MiB 128,000+ tokens 8.55 tok/s (5060 Ti) CPU offloaded; memory safe IQ4_XS-pure (Unified Memory) Overflow to RAM 0 MiB 64,000+ tokens 1.45 tok/s Fallback only; unusable for real-time 7. Inference Engine and Fork Comparison Engine / Branch Repository Core Features Build Support Stability / Compatibility Notes beellama.cpp Anbeeld/beellama.cpp kvarn2–kvarn8, precision tail, MTP, dflash Source build required Selected Engine. Native support for KVarN compression algorithms. llama-cpp-turboquant TheTom/llama-cpp-turboquant turbo2–turbo4, FA all quants Source build required Slightly faster base decode on TurboQuant, lacks KVarN implementations. buun-llama-cpp spiritbuun/buun-llama-cpp TCQ quant types, VBR, dflash optimization Source build required Validated alternative for non-standard quant formats. Stock llama.cpp b10437 Official Release Upstream standard Prebuilt Critical Bug: Segfaults in ggml_vec_dot_q3_K_q8_K on Qwen hybrid models. Stock llama.cpp b10470+ Upstream Master Upstream standard Prebuilt / Source Regression resolved; lacks kvarn and turbo KV cache types. ExLlamaV3 Official Repository Single-stream kernel acceleration Prebuilt / Wheels Incompatible with Turing (sm_75) and early hybrid attention kernels. vLLM Official Repository Continuous batching, PagedAttention Wheels / Docker High VRAM allocation baseline; unsuited for single-stream 16GB deployments. 8. Build Instructions and Runtime Configurations 8.1 Compiling beellama.cpp Build with CUDA support matching your target GPU architecture. The relevant NVIDIA RTX mappings are 75 / sm_75 for RTX 20-series (Turing), 86 / sm_86 for RTX 30-series (Ampere), 89 / sm_89 for RTX 40-series (Ada), and 120 / sm_120 for RTX 50-series (Blackwell). These are CUDA compute-capability targets used to compile the kernels, not benchmark results. Blackwell support requires CUDA 12.8 or newer. git clone https://github.com/Anbeeld/beellama.cpp.git cd beellama.cpp cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="75;86;89;120" -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA_FA_ALL_QUANTS=ON cmake --build build -j(nproc) --target llama-server llama-cli llama-perplexity 8.2 Model Acquisition Balanced Model: unsloth/Qwen3.8-27B-MTP-GGUF -> Qwen3.8-27B-IQ4_XS-pure-MTP.gguf High-Context Model: AtomicChat/Qwen3.8-27B-GGUF -> Qwen3.8-27B-AD-IQ3_S-IQ3_XXS.gguf dflash Draft Model: ggml-org/Qwen3.6-27B-GGUF -> dflash-Qwen3.6-27B-Q8_0.gguf (Optionally requantize to Q4_0 via llama-quantize --allow-requantize) 8.3 CLI Execution Flags Explained -ngl 999: Offloads all 64 model layers and prediction heads to the GPU. --cache-type-k kvarn4 --cache-type-v kvarn4: Sets 4-bit variance-normalized quantization for both Key and Value caches. --spec-type draft-mtp --spec-draft-n-max 2 --spec-draft-p-min 0.0: Configures multi-token speculative decoding to evaluate 2 candidate tokens per step with greedy acceptance. -fa on: Enables FlashAttention (mandatory for KVarN acceleration kernels). -np 1: Forces single-slot execution. Default parallel slot allocations replicate recurrent state tensors across memory, causing severe throughput regressions on hybrid architectures. --fit off: Disables automatic context/layer shedding when memory allocation approaches physical VRAM limits. -t 8: Allocates 8 CPU worker threads for host orchestration. 9. Troubleshooting and Known Pitfalls Issue Root Cause Resolution Segmentation Fault at Load Stock release b10437 bug in ggml_vec_dot_q3_K_q8_K Deploy builds ge text{b10470} or use beellama.cpp. Severe Prefill Slowdown (<10text{ tok/s}) Partial layer offload (-ngl 30) disables fused chunked Gated DeltaNet CUDA kernels Maintain full GPU offload (-ngl 999) or execute fully on CPU (-ngl 0). Throughput Regresses by ~10× Default execution assigns n_parallel > 1 Append -np 1 to force single-slot memory layout. Early Context OOM Crash Server fit algorithm sheds layers dynamically near VRAM limit Add --fit off to enforce configured allocation parameters. MTP Initialization Failure Attempting speculative execution on base non-MTP GGUFs Use GGUFs containing bundled nextn draft heads. glibc Symbol Errors on Prebuilts Binaries built on Ubuntu 24.04 (glibc 2.38) executed on Debian 12 (glibc 2.36) Compile directly from source on the target host environment. Process Termination via pkill Substring pattern matches active SSH command line arguments Target exact process names: pgrep -x llama-server / pgrep -x llama-cli. submitted by /u/MaxDev0 [link] [comments]

Source: r/LocalLLaMA | 2026-08-18