The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
A model advertised with a 128k context window can accept a large input; that number does not show how reliably it can find or use information throughout it. To find out whether a particular local model loses evidence in the middle, test the same questions at multiple context lengths and move the answer-bearing passage through several positions while keeping the rest of the setup fixed.
What a 128k context window does—and does not—tell you
The context window is an input-capacity limit, not a quality score. It says how much text, measured in tokens, a model and its inference setup can accept within one context. It does not establish that the model will retrieve every relevant detail equally well at every position, synthesize many documents accurately, or perform just as well near the limit as on a short prompt.
Actual usable capacity also depends on the full prompt: instructions, question, document separators, and any other material consume tokens alongside the evidence. Count tokens with the tokenizer or inference stack used by the model; word counts and character counts are not reliable substitutes.
Models and configurations can behave differently. A result applies to the checkpoint, quantization, tokenizer and chat template, inference engine, and settings you tested—not automatically to another model or even another configuration of the same model.
#1 Best Overall
- 【High-Performance APU】The MS-S1 MAX features an AMD Ryzen AI Max+ 395 APU, integrating a Zen 5 architecture CPU (up to 5.1GHz, 16C/32T, 64M L3 Cache), an RDNA 3.5 GPU, and an NPU (50 TOPS). The total system output is 126 TOPS. It provides powerful parallel computing capabilities for demanding AI workflows. It is ideal for running local LLMs, multimodal models, and computationally intensive tasks
- 【128GB UMA Memory】Equipped with up to 128GB of LPDDR5x-8000MT/s unified memory, it enables the CPU and GPU to access a shared, high-bandwidth memory pool with extremely low latency. Ideal for large-scale AI inference, 3D workloads, and complex timelines in video editing. It eliminates traditional VRAM bottlenecks, ensuring smoother data transfer during high-intensity computations. The UMA design maximizes performance stability under high loads
- 【Flexible Expansion】The MS-S1 MAX features USB4 V2 (up to 80Gbps), dual 10GbE LAN, HDMI 2.1 (up to 8K60), a full-length PCIe x16 expansion slot, and dual M.2 slots supporting up to 16TB RAID 0/1. Wi-Fi 7 provides stronger signal coverage and a more stable wireless experience. The slide-out design facilitates upgrades and maintenance. It easily adapts to personal, studio, or rack-mount enterprise environments
- 【High-Efficiency Cooling System】Utilizing an aerospace-grade aluminum alloy chassis, copper base plate, six heat pipes, dual turbine fans, and advanced PCM thermal conductive material, it maintains stable cooling performance even under continuous load. This system supports 130W continuous power and 160W peak power operation, with a built-in 320W power supply. It boasts multiple global certifications including CCC, FCC, UL, CE, and UKCA, ensuring stable and reliable operation in various environments
- 【Cluster Design】Two MS-S1 MAX units can be configured as a dual-unit cluster to run a large 235B Q4 model locally, achieving an output speed of 10.87 tok/s. Supporting 2U rack deployment, multiple MS-S1 MAX units can be cascaded into a distributed cluster to create a high-efficiency AI computing center. A cluster of four MS-S1 MAX units successfully ran a DeepSeek-R1 671B Q4 large model. A reserved cluster power-on interface allows for unified start-up and shutdown
What “lost in the middle” means
In “Lost in the Middle: How Language Models Use Long Contexts,” Nelson F. Liu and coauthors evaluated multi-document question answering and synthetic key-value retrieval. They found that performance could change substantially when the relevant information moved within the input. Their paper describes a common “distinctive U-shaped performance curve”: performance is often stronger when relevant information appears near the beginning or end, and weaker when it is in the middle. The pattern was observed even in models designed for long contexts. A larger context, by itself, did not ensure better use of that context.
This is a tendency reported in the study, not a rule that every model fails in the middle. The practical question is whether your chosen model and inference configuration show a meaningful position effect on the tasks you care about.
Rank #2
- Unlock next-generation AI computing with AMD Ryzen AI Max+ 395 processor featuring 16 cores, 32 threads, up to 5.1GHz boost clock, and integrated Ryzen AI engine delivering up to 126 TOPS AI performance. EVO-X3 is designed for local AI models, content creation, development, and professional workloads.
- OCuLink External GPU Expansion – Upgrade Beyond a Mini PC: Take your graphics performance further with a dedicated OCuLink (PCIe 4.0 x4) interface. Connect an external GPU dock to add desktop-class graphics power for AAA gaming, AI acceleration, 3D rendering, video production, and advanced creative applications. EVO-X3 gives you the flexibility of a compact PC with workstation-level expansion capability.
- 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 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, 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; 12% better performance in digital content workloads.
How to test whether a local LLM loses information in the middle
Run a controlled test: keep the question and distractors stable, move the same answer-bearing passage to different locations, and repeat at multiple total input lengths. Change one factor at a time so a change in score can be interpreted.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
- Choose a task and write an answer key. Start with a question whose answer is explicitly stated in one passage. Define acceptable answers before running the model. For a realistic check, also prepare multi-document questions that require combining evidence; score that task separately from direct lookup.
- Build matched prompts. Use the same question, answer-bearing passage, distractor material, instructions, and formatting in each version. Place the answer-bearing passage near the beginning, at several intermediate positions, and near the end. Avoid changing the wording of the evidence as you move it.
- Repeat at several input lengths. Include a short prompt, a medium-length prompt, and a prompt approaching the advertised limit. Keep the relevant passage and question the same; vary the amount of distractor content. Check the final token count with the model’s tokenizer or inference stack, and leave room for the requested output and any template overhead.
- Hold the configuration fixed. Record and keep constant the exact model checkpoint, quantization, tokenizer and chat template, inference engine, sampling settings, and prompt. If you change any of these, treat the result as a separate test rather than a direct comparison.
- Run and score every case. Compare answers with the predefined key. Record incorrect answers, missing answers, and refusals rather than counting only successful retrievals. If a task permits several equivalent phrasings, specify the scoring rule in advance.
- Compare position and length. Plot accuracy by answer position at each context length. Report the best and worst position scores and their gap, as well as overall accuracy. Keep single-fact retrieval and multi-document QA results separate.
Use controlled tasks, but don’t mistake them for the whole job
The Lost in the Middle authors’ repository provides useful starting materials: QA tasks with one answer-bearing passage among 10, 20, or 30 documents, and key-value retrieval data and procedures. In the key-value task, records contain ordered keys and values, and the model must return the value for a requested key. These formats make it possible to move the relevant evidence while controlling other parts of the input. Check software and model compatibility before using the materials.
Synthetic key-value lookup is a clean way to probe whether position affects finding a specific item. Multi-document QA is closer to finding evidence among documents. Neither alone establishes broad comprehension or reliable synthesis across the full context. If both matter for your use case, test both and present their results separately.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret a needle-in-a-haystack score
A needle test inserts a fact into a larger body of text and asks a direct question about it. It can reveal whether a model retrieves a particular fact under the tested conditions, especially when the fact is moved to different positions. One successful run—or a high score at one position—does not show that the model handles every position, longer inputs, multiple relevant documents, or questions that require synthesis.
Rank #4
- 【Leading AI Mini Workstation】MINISFORUM AI MS-S1 Max Workstation comes with AMD Ryzen AI Max+ 395 processor, which uses AMD's latest generation Zen 5 architecture. It has 16 Cores and 32 Threads, the boost clock is up to 5.1GHz. The overall processor performance is up to 126 TOPS, and the NPU performance reaches up to 50 TOPS. AMD Ryzen AI enables improved productivity, advanced collaboration, and improved efficiency.
- 【AMD Radeon 8060S Graphics 】The MS-S1 Max Mini PC equipped with AMD Radeon 8060S Graphics which built on the new generation of RDNA 3.5 architecture AMD graphics, it brings ultra-high frame rate experiences and advanced content creation features anywhere and delivers staggering performance. It can handle all your computing and multimedia tasks efficiently.
- 【Five 8K Video Output】This MS-S1 Max Workstation comes with five video outputs, 1x HDMI (8K@60Hz), 2x USB4(40Gbps,Alt DP2.0,PD out 15W) and 2x USB4 V2(80Gbps,Alt DP2.0,PD out 15W) Outputs, which support multiple monitors display at the same time and provide a larger and wider filed of view and improve your work efficiency. It is used in fields that require high-performance computing and graphics processing, including digital signage and securities trading, as well as work that uses CAD, such as engineering design, scientific calculations, animation production, and post-production for movies and television.
- 【 Fast and Stable Wire & Wireless Speed】It comes with Two 10G Lan Ports for wired connection and and Wi-Fi 7 / BT5.4 for wireless connection, which increased the network speed greatly and expand its functions and improved performance of computer to a large extent and allows you to use more networks such as software routers (OpenWRT / DD-WRT / Tomato etc.), firewalls, NAT, network isolation etc.
- 【Large Storage & Flexible Expandability】This Workstation equipped with 128GB LPDDR5-8000MHz + 2TB M.2 2280 PCIe4.0 SSD. There is another PCIe4.0 SSD slot available for up to 8TB, these SSD slots are compatible with RAID0 and RAID1, you can store movies, videos, photos, important files easily. What’s more, it also comes with 1x standard PCIex16 slot(PCIe4.0x4) inside.
A 2025 preprint, “Retrieval Quality at Context Limit,” reported that Gemini 2.5 Flash answered all 26 injected single-fact questions correctly at tested context sizes up to 92% of its context limit; the test exceeded the limit at the next step. The setup used a Friends transcript, inserted 20 fictional facts, and tested direct factoid retrieval. That is a bounded result for a hosted model and a specific task, not evidence that local models behave the same way or that positional sensitivity is absent in other tasks.
What to include in a credible result
A context-window claim is more useful when readers can see what was tested and where it worked. Report:
- Exact model checkpoint, quantization, tokenizer or chat template, inference engine, and sampling settings.
- Input lengths in tokens and how token counts were measured.
- Task type, number of cases, answer key or scoring rule, and how refusals or missing answers were counted.
- Accuracy at each tested answer position and context length, not just the strongest score.
- The best-to-worst position gap for each context length.
- Whether the test was synthetic key-value retrieval, direct single-fact lookup, multi-document QA, or another task.
Liu and coauthors frame robust long-context use in position-sensitive terms: “to claim that a language model can robustly use information within long input contexts, it is necessary to show that its performance is minimally affected by the position of the relevant information in the input context (e.g., minimal difference in best- and worst-case performance).” In other words, a claimed context limit is not a substitute for measuring how much performance changes across the input.
How to compare two local model configurations fairly
Use the same prompts, task set, token lengths, answer positions, scoring rules, and test conditions for both configurations. Compare accuracy by position and length, the best-to-worst position gap, and results for retrieval versus multi-document QA. Identify each checkpoint, quantization, and inference setup precisely. Without controlled runs, a ranking of local models is not established by context-window size alone.
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.
