Mistral-Nemo-Instruct-2407 · GGUF Q8_0

Evaluation results: this Q8_0 build compared with F16, capability family by family, with confidence intervals and file size, on the results page: pbhappliedsystems.com/models/mistral-nemo-instruct-2407#Q8_0

Does Q8_0 quantization degrade Mistral-Nemo-Instruct-2407?

Answer (quant_eval v7.22.22): Against its own F16 baseline, Mistral-Nemo-Instruct-2407 at Q8_0 degraded 0 of 8 capability families and improved 0 (McNemar, p < 0.05; n = 200 per family). No family degraded significantly; the largest movement, toolcall_only at −0.5 points, is within noise. Full results with intervals

Licence provenance: Apache-2.0 (SPDX Apache-2.0), commercial use permitted_by_recorded_license; upstream mistralai/Mistral-Nemo-Instruct-2407 at revision 8aedd450f2583e9c67fae1929f6936b8fc5aef9c (exact); evaluated file SHA-256 e2913d66eee3b39f2769b830ade9284d27b31a1adcc20d99de87b75f632c7427.

Other builds of this model: F16 · Q4_K_M · Q5_K_M · All evaluated builds (Collection) · Datasets

Contents: Model Description · PBH Applied Systems Evaluation — quant_eval v7.22.22 · Comparability & Fixture Methodology · Per-Family Paired Comparison — F16 Baseline vs. Q8_0 · Key Findings · Efficiency & Footprint · Quantization Variants Comparison · Artifact Provenance

Quantized, converted, and evaluated by PBH Applied Systems, LLC — Applied AI/ML Consulting · LLM Optimization & Deployment · Quantized AI Infrastructure

🔬 This repository is part of a production-oriented evaluation series. Every model published under pbhappliedsystems has been independently evaluated using quant_eval v7.22.21/v7.22.22 — a behavioral evaluation harness developed by PBH Applied Systems. Scores measure real agent-adjacent task performance across structured output, tool dispatch, multi-turn state retention, and multi-step planning families — not perplexity or benchmark leaderboard proxies.

✓ No statistically significant degradation from F16 in any family (McNemar p < 0.05: 0 of 8). Stateful execution (1.000), hybrid responses (1.000), and end-to-end tool calls (0.930) match F16 case for case; schema-only tool calls differ by a single case (0.995 vs 1.000). Q8_0 is the conservative quantization: it preserves measured behavior in exchange for more modest footprint and speed gains.

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Model Description

This repository contains the 8-bit quantized (Q8_0) GGUF of mistralai/Mistral-Nemo-Instruct-2407, a 12-billion parameter instruction-tuned model developed by Mistral AI in collaboration with NVIDIA (July 2024 release). Mistral-Nemo features the Tekken tokenizer and a 128,000-token context window — the largest in the PBH Applied Systems evaluated series outside of Qwen2.5-14B-Instruct-1M, which supports a 1 million token window.

The Q8_0 format applies 8-bit quantization. Unlike Q4_K_M, which introduces significant behavioral tradeoffs, Q8_0 matches F16 case for case on stateful execution, end-to-end tool dispatch, and hybrid responses — families central to agentic AI workflows. This is the quantization to choose when behavioral fidelity and model reliability are the top priorities, with efficiency gains as a secondary benefit.

The full-precision F16 baseline is published separately at pbhappliedsystems/mistral-nemo-instruct-2407-gguf-F16.

Key Characteristics

• Parameters: 12B
• Format: GGUF Q8_0
• File size: 13.0 GB
• SHA256: e2913d66eee3b39f2769b830ade9284d27b31a1adcc20d99de87b75f632c7427
• Context window: 128,000 tokens (Tekken tokenizer)
• Minimum VRAM (GPU inference): ~15 GB (16 GB-class GPUs or larger)
• Recommended GPU tier: A10G (24 GB) · RTX 4090 (24 GB) · A100 40GB
• Inference speed (eval hardware): avg 58.86 tokens/sec on NVIDIA RTX 4090
• Speedup vs F16 (same run, same hardware): 1.78× generation throughput (33.11 → 58.86 tokens/sec); 1.71× observed evaluation wall-time ratio
• Multilingual: English, French, German, Spanish, Italian, Portuguese, Russian, Chinese, Japanese

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PBH Applied Systems Evaluation — quant_eval v7.22.22

Evaluation conducted by PBH Applied Systems, LLC using quant_eval v7.22.22 Run ID: Mistral_Nemo_Instruct_2407_20260816_084553 · Fixtures: v7.22.22 (SHA256: 0673b5e...) Evaluation date: August 16, 2026 · Seed: 42 · Hardware: NVIDIA RTX 4090 · Published: August 16, 2026

Evaluation Data Published to Zenodo

All evaluation artifacts are published under the quant_eval Public Corpus (DOI: 10.5281/zenodo.22009419):

• Paired Degradation Statistics (D5): 10.5281/zenodo.22010557 — McNemar tests, Wilson intervals, cluster-adjusted paired deltas across all evaluated precisions
• Run Provenance (D4): 10.5281/zenodo.22010462 — Selected run manifest and rollup fields, artifact SHA-256 hashes, runner and decoding configurations
• Golden Oracle Fixtures (D3): 10.5281/zenodo.22010278 — Test case specifications and fixture version crosswalk
• Per-Case Behavioral Results (D1): 10.5281/zenodo.22009799 — 19,200 per-case rows, both precisions, all signals, case-level pass/fail
• Family Pass Rates (D6): 10.5281/zenodo.22010623 — Aggregate pass rates per family, both runners, 95% Wilson confidence intervals
• Throughput Telemetry (D2): 10.5281/zenodo.22009987 — Per-call generation metrics, wall-time, token counts
• Efficiency & Footprint (D7): 10.5281/zenodo.22010723 — Stored artifact size, compression ratio, tokens/sec, observed wall-time ratios with hardware scope

Evaluation Contract: canonical_agentic_contract_v7.22.18:evaluation

Direct comparability: Q8_0, Q5_K_M, and Q4_K_M were all evaluated on identical fixtures against the same F16 baseline. Q8_0 and Q5_K_M both show no statistically significant degradation; Q5_K_M is faster (2.65× vs 1.78× same-run generation throughput) and smaller, while Q8_0 is the more conservative choice.

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Comparability & Fixture Methodology

Identical Fixtures Across All Three Runs

All three Mistral-Nemo runs were evaluated against the same 1,600 cases. A full structural comparison of the fixture files across bundles shows the only differing key is the top-level version label: all cases, oracle expectations, and trace_sha256 values are identical, and fixture_schema_version is 7.22.13 in every bundle.

Baseline reproducibility: The F16 baseline was executed in all three runs, on different days and under both contract revisions. All eight family pass rates are identical across the three runs, and at case level 0 of 1,600 cases differ in any pairwise comparison. The Q4_K_M, Q5_K_M, and Q8_0 deltas are all measured against the same baseline.

The Zenodo datasets record the fixture version-label crosswalk (D3) and per-run manifests (D4) for full reproducibility.

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Per-Family Paired Comparison — F16 Baseline vs. Q8_0

All rates include 95% Wilson score confidence intervals. McNemar p-values test statistical significance of paired differences. N = 200 cases per family.

* mcq pass rates are identical, but two cases are discordant (one F16-only pass, one Q8_0-only pass). The other five "PERFECT MATCH" families are identical case for case.

Sources:

• quant_eval Family Pass Rates, DOI 10.5281/zenodo.22010623
• quant_eval Paired Degradation Statistics, DOI 10.5281/zenodo.22010557

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Key Findings

Finding 1: Six Families — Identical F16 Pass Rates

Q8_0 matches F16's pass rate on six of eight families (five identical case for case; mcq has one discordant case in each direction):

Why it matters: Q8_0 matches F16 case for case on stateful, hybrid, and end-to-end tool-calling families, and matches F16's pass rates on all structured-output families. No family shows a statistically significant behavioral cost. This is the quantization to choose when reliability and model fidelity are the priority.

Implication: Deploy Q8_0 as you would deploy F16 — with the same validation F16 itself requires, and none of the additional guardrails Q4_K_M needs.

Finding 2: Negligible Loss on Two Families

Two families show minor, statistically non-significant differences:

Why it matters: The McNemar p-values (1.000) indicate these differences are noise, not signal. One case in toolcall_only failed under Q8_0 where F16 succeeded — a data point, not a trend. Fuzz actually shows a tiny gain, well within variance.

Implication: Operationally equivalent to F16 on all eight families.

Finding 3: Conservative Quantization — The Right Choice for High-Stakes Deployments

Q8_0 is the quantization for:

• Production agents where failures are costly
• Workflows where reliability >> speed
• Teams without advanced fallback/retry infrastructure
• Models deployed to constrained but capable hardware (~15 GB VRAM available)

The 1.78× same-run generation throughput (vs F16) and 47% footprint reduction are real. No statistically significant behavioral loss is the gain that matters.

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Efficiency & Footprint

Source: quant_eval Efficiency & Footprint, DOI 10.5281/zenodo.22010723

Q8_0 trades maximum efficiency for maximum reliability. The speedup and footprint reduction are meaningful but not extreme. This is the quantization when the goal is "get better hardware utilization without breaking the model."

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Quantization Variants Comparison

See how this model performs across all three quantized variants. All three were evaluated on identical fixtures against the same F16 baseline.

How to read this table: Speed is the observed evaluation wall-time ratio (F16 ÷ quantized) on matched RTX 4090 hardware — not a controlled throughput benchmark. Stateful/Toolcall-only/Hybrid are pass rates.

See the full cards:

• F16 — Baseline reference
• Q4_K_M — Extreme compression, significant behavioral cost
• Q5_K_M — Balanced quantization, best efficiency/reliability ratio

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Artifact Provenance

Both artifacts were produced from mistralai/Mistral-Nemo-Instruct-2407 using a custom-built llama.cpp conversion and quantization pipeline developed by PBH Applied Systems.

Artifact verification: SHA256 hashes are recorded in the run manifest (DOI 10.5281/zenodo.22010462). Download the Q8_0 GGUF and verify:

sha256sum Mistral_Nemo_Instruct_2407_Q8_0.gguf
# Should output: e2913d66eee3b39f2769b830ade9284d27b31a1adcc20d99de87b75f632c7427

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Evaluation Methodology

quant_eval v7.22.22 is a behavioral evaluation harness developed by PBH Applied Systems. The two-run architecture evaluates both the full-precision (F16) and quantized variant against an identical fixture set, enabling direct comparison of behavioral degradation.

Fixture set: v7.22.22 evaluation split

• SHA256: 0673b5e5c0fb1b7ba4bdc8c9aa985104260db183453c5708aff14c87823edb97
• Total unique cases: 1,600 (200 per family × 8 families)

Evaluation hardware: NVIDIA RTX 4090 (24 GB VRAM) Runner: quantized_llama_cpp (llama.cpp via Python binding, version 0.3.16) Decoding: temperature 0.3 (per Mistral AI's published recommendation), seed 42, top_p 1.0 Context: 4,096-token context window (llama.cpp configuration) Reproducibility: Run manifest, fixtures, per-case results, and paired statistics published to Zenodo under the quant_eval Public Corpus

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Deployment Recommendations

✅ Deploy Q8_0 when:

• Behavioral reliability is the priority. Six families match F16's pass rate; no family shows a statistically significant difference.
• You can afford ~15 GB VRAM. Q8_0 fits on 16 GB-class GPUs and larger (A10G, RTX 4090, A100 40GB).
• Simplicity matters. Deploy as you would F16 — no additional retry or fallback logic beyond what F16 itself requires.
• Your workflow uses stateful conversations. Perfect performance on stateful_followup (1.000).
• Tool calling must match F16. End-to-end toolcall is 0.930 — identical to F16, case for case.
• You want the lowest measured quantization risk. Q8_0 shows the smallest deviation from F16 of the three variants evaluated.

⚠️ Avoid Q8_0 if:

• Extreme efficiency is the goal. Q5_K_M is faster (2.56× vs 1.71× observed wall-time ratio vs F16) and smaller (8.73 GB vs 13.0 GB); Q4_K_M is the smallest (7.48 GB).
• You need maximum speed at acceptable reliability. Q5_K_M is the better choice — faster, with no statistically significant degradation in any family.
• Hardware is extremely constrained. Q4_K_M fits in ~10 GB; Q8_0 requires ~15 GB.

🎯 Recommended Deployment:

Q8_0 is the reliability-first quantization. Use it for:

• Production agents with high uptime / low-tolerance-for-failure requirements
• Regulated environments requiring documented model behavior preservation
• Teams prioritizing operational simplicity over extreme efficiency
• Scenarios where one agent failure blocks critical workflows

Q5_K_M requires less VRAM (~11 GB vs ~15 GB) and offers better efficiency, also without statistically significant degradation. Q8_0 is the choice when you want the smallest measured deviation from F16.

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About PBH Applied Systems

PBH Applied Systems, LLC is an Oklahoma City–based applied machine learning and AI systems company specializing in production-grade model evaluation, quantization pipelines, agentic AI infrastructure, and scalable AI-driven application development. The organization emphasizes engineering rigor, reproducibility, and real-world deployment constraints — particularly in environments where performance, cost efficiency, and reliability must be balanced against available hardware and budget.

Founder & Principal AI/ML Systems Architect — Patrick Hill

Patrick Hill is the Founder & Principal AI/ML Systems Architect of PBH Applied Systems with 10+ years of experience delivering advanced analytics, predictive modeling, and decision-support solutions across high-stakes operational environments. Patrick holds a Master of Science in Software Engineering with concentrations in Artificial Intelligence and Machine Learning and a B.S. in Business Finance.

Technical expertise spans: Python, SQL, Linux, Pandas, NumPy, scikit-learn, PyTorch, TensorFlow/Keras, HuggingFace Transformers, GGUF, llama.cpp, BitsAndBytes, PEFT, QLoRA, Flask APIs, Docker, CI/CD, Jupyter, Databricks, and quantization strategies.

Published Author: Patrick is the author of Applied Machine Learning: Concepts, Tools, and Case Studies — a 1,200+ page practitioner-oriented textbook adopted as required reading for CSC 373 – Machine Learning at the University of Advancing Technology.

Core Service Areas: LLM optimization & deployment · AI evaluation frameworks · Agentic AI infrastructure · Scalable AI application development · ML pipeline design & analytics · Model & agent cataloging.

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🔬 About quant_eval & This Evaluation Series

quant_eval is a behavioral evaluation harness developed by PBH Applied Systems, LLC. It measures real agent-adjacent task performance across structured output, tool dispatch, multi-turn state retention, and multi-step planning — not perplexity or leaderboard proxies. Every model published under pbhappliedsystems has been independently evaluated using quant_eval before being recommended for any production role.

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License

This GGUF repository inherits the license of the base model: Apache 2.0 — mistralai/Mistral-Nemo-Instruct-2407

The quant_eval evaluation harness, fuzz prompt builder, and scoring implementation are proprietary to PBH Applied Systems, LLC and are not included in this repository. The golden oracle fixture set used in this evaluation is published under CC BY 4.0 as part of the quant_eval Public Corpus (D3, DOI 10.5281/zenodo.22010278).

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GGUF conversion, quantization, and behavioral evaluation performed by PBH Applied Systems, LLC · quant_eval v7.22.22 · Run ID: Mistral_Nemo_Instruct_2407_20260816_084553 · Published 2026-08-16