Free tools Windows power users keep installed
One-click scans. No signup required.
A quantized local LLM can give a different answer because quantization approximates the model’s weights, which can shift the scores it assigns to likely next tokens. A small shift can change one selected token and send the rest of the response in a different direction. That difference alone does not show that the quantized model is meaningfully worse: sampling, prompt formatting, model versions, and runtime settings can also change outputs. To judge quality, compare the same model under controlled settings and test tasks you actually care about.
Why does my quantized local LLM give different answers?
Quantization stores model weights with lower numerical precision, often using scales and groups to represent many values compactly. During inference, the model uses those approximated values, either through dequantization or quantized operations. Approximation can shift internal calculations and the logits—the scores associated with possible next tokens.
If two candidate tokens have similar scores, even a modest shift can change which one is selected. The model then conditions on that token when generating the next one, so later text may diverge substantially. The visible difference can therefore be much larger than the initial numerical change. The Qwen quantization guide describes weight quantization, mixed quantization types, and techniques such as calibration or an importance matrix that can help protect sensitive weights.
Sampling can cause differences even without quantization
Quantization is not the only possible cause. If decoding uses temperature or another sampling control that permits randomness, two runs can differ even with identical weights. For a fair comparison, use greedy decoding or another deterministic mode if your runtime supports it. Otherwise, fix and report the seed where possible, and repeat runs to see how much variation occurs. Do not assume a runtime guarantees bit-for-bit identical results across hardware or software versions unless that behavior is documented.
Recommended Free Tools
#1 Best Overall
- EVOLUTION AMD RYZEN AI MAX+ 395 MINI PC - GMKtec EVO-X2 is the next evolution in AI mini PC Ryzen Strix Halo series. Thanks to AMD Simultaneous Multithreading (SMT) the core-count is effectively doubled, to 32 threads. Ryzen AI Max+ 395 has 64 MB of L3 cache and can boost up to 5.1 GHz, depending on the workload. The Ryzen AI Max+ 395 is currently rated as the "most powerful x86 APU" on the market for AI computing.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 64GB pool, which is perfect for running LLMs such as Deepseek 32B, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 4% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
Keep the comparison controlled
Use the same prompt, system message, chat template, tokenizer, context, stop rules, and runtime settings for both models. A change in any of these can affect the answer, making it harder to isolate quantization as the cause.
Is a 4-bit model worse than the original?
Not as a universal rule. Quality depends on the model, quantization method, bit width, calibration, task, and inference implementation. Research comparing quantization strategies and instruction-tuned models reports results that vary across methods, model sizes, bit widths, and benchmarks; it does not establish one quality-loss percentage that applies to all models. See the evaluations in A Comprehensive Evaluation of Quantization Strategies for Large Language Models and A Comprehensive Evaluation of Quantized Instruction-Tuned Large Language Models.
Meta’s Llama 3.2 model card illustrates why comparisons need precise scope. For its 1B Instruct evaluation, the card reports MMLU (5-shot) scores of 49.3 for BF16 and 43.3 for Vanilla PTQ, and IFEval (0-shot) scores of 59.5 and 51.5, respectively. Those figures apply to that model and the card’s evaluation setup; the card notes that the Vanilla PTQ comparison model is not released. They are not estimates for another model, quantization format, runtime, or task.
Rank #2
- 𝗔𝟵 𝗠𝗮𝘅 𝗔𝗜𝟵 𝟰𝟳𝟬 – 𝗙𝗹𝗮𝗴𝘀𝗵𝗶𝗽 𝗔𝗜 & 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗪𝗼𝗿𝗸𝘀𝘁𝗮𝘁𝗶𝗼𝗻 - The GEEKOM A9 Max now features the AMD Ryzen AI 9 470, built on AMD’s latest Strix Point architecture. Delivering up to 86 TOPS AI acceleration, including an XDNA 2 NPU rated up to 55 TOPS, this compact mini PC transforms how professionals handle demanding workloads. From running large enterprise AI models and local LLMs to producing 8K video content and advanced 3D rendering, the A9 Max ensures smooth, uninterrupted performance. Perfect for enterprise AI projects, financial analysis, scientific research, professional content creation, educational labs.
- 𝗔𝗔𝗔 𝗚𝗮𝗺𝗶𝗻𝗴 𝗨𝗻𝗹𝗲𝗮𝘀𝗵𝗲𝗱—𝗨𝗽 𝘁𝗼 𝟭𝟯𝟬 𝗙𝗣𝗦 𝘄𝗶𝘁𝗵 𝗜𝗰𝗲𝗕𝗹𝗮𝘀𝘁 𝟯.𝟬 – Powered by AMD Ryzen AI 9 HX 470 (12C/24T, up to 5.2GHz), Radeon 890M Graphics, the GEEKOM A9MAX is built for smooth 1080p AAA gaming, streaming and 4K creation. Radeon 890M platforms have demonstrated up to 90 FPS in Cyberpunk 2077, 99 FPS in Forza Horizon 5 and 130 FPS in F1 24 with optimized settings and supported upscaling or frame generation. The all-metal chassis and IceBlast 3.0 cooling system combine a large copper heatsink, dual heat pipes and a quiet fan, with Standard and Performance modes to help maintain stable performance during long gaming, editing and rendering sessions.
- 𝗛𝗶𝗴𝗵-𝗦𝗽𝗲𝗲𝗱 𝗗𝗗𝗥𝟱 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗘𝘅𝗽𝗮𝗻𝗱𝗮𝗯𝗹𝗲 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 - Preinstalled with 32GB DDR5 RAM (expandable to 128GB) and equipped with dual PCIe Gen4 NVMe SSD slots (1× M.2 2280 + 1× M.2 2230, up to 8TB total), the A9 Max supports high-capacity storage for large datasets, high-speed scratch disks, and multiple simultaneous workloads. Run AI models, process high-resolution media, or simulate complex projects without delays. This ensures a smooth, responsive, and efficient workflow, enabling professionals to focus on creative and analytical tasks without interruptions.
- 𝟰-𝗗𝗶𝘀𝗽𝗹𝗮𝘆 𝟴𝗞 𝗩𝗶𝘀𝘂𝗮𝗹𝘀 & 𝗗𝘂𝗮𝗹 𝟮.𝟱𝗚𝗯𝗘 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 – Powered by AMD Radeon 890M graphics, GEEKOM A9 Max supports up to four independent displays and 8K output, creating a professional multi-screen workstation without a docking station. Handle financial dashboards, 8K video editing, AI image generation, CAD design, and 3D rendering with ease. Featuring USB4, HDMI 2.1, dual 2.5GbE LAN, WiFi 7, and 3D Stereo WiFi Antenna, it provides stronger signal coverage, fewer dead zones, and more stable wireless connectivity for AI development, creative studios, research labs, and enterprise deployments.
- 𝗨𝗽 𝘁𝗼 𝟱𝟱 𝗧𝗢𝗣𝗦 𝗡𝗣𝗨 𝗳𝗼𝗿 𝗛𝗶𝗴𝗵-𝗖𝗼𝗺𝗽𝘂𝘁𝗲 𝗟𝗼𝗰𝗮𝗹 & 𝗖𝗹𝗼𝘂𝗱 𝗔𝗜 – Combining a 12-core CPU, Radeon 890M graphics and a dedicated NPU, this compact PC supports compatible quantized LLMs and VLMs for batch document intelligence, large-codebase analysis, multi-stream computer vision, generative design and multimodal research. Enterprises can process R&D datasets, proprietary code, financial models and confidential media locally; engineers, developers and creators can accelerate AI prototyping, 8K production, 3D rendering and simulation. Sensitive workloads can remain on-device, while cloud AI adds larger models and deeper reasoning when needed.
Bit width alone is also not enough to identify quality. Quantization files can use mixed precision, different groupings, calibration, or importance-based choices. Check the exact format and how it was produced rather than treating every file described as “4-bit” as interchangeable.
How do I test whether a GGUF quant is any good?
Compare it with the higher-precision checkpoint from which it was made, if available, and separate quality from resource use. This workflow helps identify whether a quantized GGUF works well for your intended tasks without mistaking a different checkpoint or generation setting for a quantization effect.
1. Confirm that the models are comparable
- Check the model family and revision, and whether each file is base or instruction-tuned.
- Confirm that both use the same tokenizer and chat template.
- Record the exact quantization type and runtime. A comparison between different checkpoints, tokenizers, or templates cannot isolate the effect of quantization.
The Llama 3.2 model card is an example of why scheme and setup matter: its reported quantization scheme is designed for a particular inference framework and Arm CPU backend.
Rank #3
- EVOLUTION AMD RYZEN AI MAX+ 395 MINI PC - GMKtec EVO-X2 is the next evolution in AI mini PC Ryzen Strix Halo series. Thanks to AMD Simultaneous Multithreading (SMT) the core-count is effectively doubled, to 32 threads. Ryzen AI Max+ 395 has 64 MB of L3 cache and can boost up to 5.1 GHz, depending on the workload. The Ryzen AI Max+ 395 is currently rated as the "most powerful x86 APU" on the market for AI computing.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 64GB pool, which is perfect for running LLMs such as Deepseek 32B, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 4% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
2. Build a representative prompt set
Choose a fixed set of prompts matching the work you expect the model to do. Depending on your use case, include factual questions, domain-specific examples, instruction following, structured output, code, or long-context retrieval. Add expected answers or a scoring rubric where practical; do not decide based on a single showcase prompt.
If you use a public benchmark, report its name, dataset split, prompt or few-shot configuration, and scoring method. For a high-stakes use case, include human review and an appropriate domain-specific evaluation rather than relying on an automatic score alone.
The Tool Desk
Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →3. Run both models with the same settings
Send identical prompts to each model and match the generation settings as closely as possible. Record the runtime and version, hardware, context limit, decoding parameters, seed or repeat policy, system prompt, and any other settings that can affect output.
Rank #4
- Built for Local AI and Advanced Workflows – The BOSGAME M5 AI Mini PC is powered by AMD Ryzen AI Max+ 395 with 16 cores, 32 threads, up to 5.1GHz, 50 TOPS NPU performance and up to 126 TOPS total AI performance. It is designed for local AI inference, private AI assistants, coding, data analysis, virtualization, content creation and demanding multitasking while keeping sensitive data on the device.
- 128GB Unified Memory for Large Models and Creative Projects – M5 includes 128GB LPDDR5X-8000 unified memory, giving the CPU and Radeon 8060S graphics access to a large shared memory pool. This helps support memory-intensive AI workloads, large project files, multiple virtual machines, 3D work, video editing and complex professional applications without the capacity limits of typical 32GB or 64GB mini computers.
- Radeon 8060S Graphics for Creation, Rendering and Gaming – Integrated Radeon 8060S graphics with 40 RDNA 3.5 compute units delivers high-end visual performance without a separate graphics card. Use the M5 creator workstation for 4K video editing, 3D rendering, CAD, AI image workflows, high-resolution media and modern gaming, while maintaining a compact desktop footprint.
- 2TB PCIe 4.0 SSD and Flexible Expansion – A pre-installed 2TB NVMe PCIe 4.0 SSD provides fast access to models, datasets, media libraries and project files. A second M.2 2280 PCIe 4.0 slot allows additional storage expansion, while the SD 4.0 card reader supports efficient photo and video workflows for creators and production teams.
- Professional Connectivity and Four-Display Support – Dual USB4 ports, HDMI 2.1 and DisplayPort 1.4 support up to four displays and resolutions up to 8K@60Hz. WiFi 7, Bluetooth 5.4 and 2.5GbE deliver fast networking for cloud collaboration, NAS access and business deployment. Windows 11 Pro, performance-mode switching, Wake-on-LAN and auto power-on support flexible workstation use.
Judge correctness and task success, not just textual similarity. Two valid paraphrases can differ in wording while being equally useful. If sampling is enabled, run repeated comparisons under the same documented policy so ordinary generation variability is not mistaken for a quantization effect.
4. Use perplexity or KL divergence as diagnostics
Perplexity estimates how well a model predicts the next token in a corpus; lower values are generally better when comparing the same model and tokenizer under comparable conditions. The llama.cpp perplexity documentation describes using it primarily to assess loss from quantization against FP16. It cautions that values are not directly comparable across different tokenizers and that implementation details affect exact results.
For a closer comparison with a reference model, llama.cpp can record reference logits and calculate KL divergence for the quantized model. A value of zero means the distributions are identical in that comparison. The documentation also discusses changes in probability assigned to the correct token and percentiles that can help distinguish broadly noisy shifts from one-sided degradation. These are diagnostic measures of prediction or distribution changes, not proof that a model will perform well on your tasks.
Best Value
- LOW ENERGY HIGH PERFORMANCE MINI PC - The Intel Core Ultra 5 125U is part of the Ultra 5 lineup, using the Meteor Lake architecture with BGA 2049. Intel Hyper-Threading technology is available and effectly doubles the core-count of the P-Cores, to a total of 14 threads. Core Ultra 5 125U has 12 MB of L3 cache and operates at 1300 MHz by default, but can boost up to 4.3 GHz, depending on the workload. With a TDP of 15 W, the Core Ultra 5 125U consumes very little energy but outputs high performance efficiency
- 32GB DDR5 RAM + 512GB SSD - The K15 mini computer is equipped with Dual 16GB (Total 32GB) SO-DIMM DDR5 4800MHz memory sticks. 512GB PCIE 4.0 SSD Drive with 3x M.2 2280 Expansion slots. Each slot capable of reading up to 8TB. (24TB MAX)
- QUAD SCREEN 4K DISPLAY SUPPORT - K15 Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and USB Type-C Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support
- OCULINK PORT - The Oculink port on the rear interface enables higher bandwidth capabilities, better frame rates and lower lag. The standard also operates at PCIe x4 speeds, compared to Thunderbolt's x3. Gamers and content creators can benefit from Oculink's higher bandwidth, resulting in better performance and lower lag for eGPU setups
- DUAL NIC FAST 2.5GBE + WIFI 6E + BT 5.2 - Dual Ethernet 2.5GbE LAN port design provides more applications, such as firewall, multichannel aggregation, soft routing, file storage server. Built-in WIFI 6E / Bluetooth 5.2 is more stable and efficient to connect multiple wireless devices such as projector, printer, monitor, speakers and etc
Use the same corpus and preprocessing for both models. WikiText-2 appears in llama.cpp’s documentation as a common comparison set for base models, but the Qwen guide cautions that it is not a good evaluation set for instruction models. For an instruct or chat model, choose data closer to its intended use.
5. Check task quality and resource tradeoffs separately
Add task-level checks for the capabilities that matter to you, such as accuracy, instruction following, formatting, reasoning, or tool behavior. The quantization-strategy evaluation organizes its comparisons around knowledge and capacity, alignment, and efficiency. Another broad evaluation of instruction-tuned models finds benchmark outcomes vary by method, model size, bit width, and benchmark, and notes that MT-Bench has limited power to distinguish among strong recent models.
Measure memory or disk use and throughput alongside quality, but do not assume a smaller model file will be faster on your hardware. An Android Open Source Project mirror of the llama.cpp quantization README, tagged studio-2026.1.1, lists these file-size examples:
| Model | Original model size | Q4_K_M model size |
|---|---|---|
| Llama 3.1 8B | 32.1 GB | 4.9 GB |
| Llama 3.1 70B | 280.9 GB | 43.1 GB |
These are documented file sizes for the named models and format, not guaranteed RAM requirements or a general sizing formula. Actual runtime memory also depends on factors such as context and the KV cache.
Windows Errors? Fix Them Before They Spread
Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallCrashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteWhat should I record so the result is reproducible?
Keep enough information for someone else to understand what was compared and repeat the test. A short run log can capture:
- Model name, revision, source checkpoint, and base or instruction-tuned variant.
- Quantization format and provenance of the file.
- Tokenizer, chat template, runtime and version.
- Prompt set or benchmark, dataset split, preprocessing, and scoring method.
- System prompt, context limit, stop rules, decoding parameters, and seed or repetition policy.
- Hardware, quality results, file or memory use, and throughput.
When comparing quantizations, weigh task-specific quality, memory and disk size, prompt-processing and generation speed on the same hardware, runtime compatibility, and file provenance. No single bit-width label captures all of those tradeoffs.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
