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Yes. A Ryzen AI MAX+ 395 mini PC can run large language models (LLMs) locally, including large quantized models, because its integrated Radeon 8060S GPU shares up to 128GB of LPDDR5x memory with the CPU. AMD says up to 96GB of that memory can be assigned as graphics memory with Variable Graphics Memory. A 128GB system has enough capacity for model classes such as 70B and, in AMD’s published 2026 tests, a 120B model. That does not mean every model will fit, load quickly, or generate tokens at a useful speed: quantization, context length, runtime, memory allocation, cooling and power limits determine the actual result.
Why the Ryzen AI MAX+ 395 is suitable for local LLMs
The key advantage is not simply the processor’s core count. It is the size and bandwidth of its unified memory pool. The Ryzen AI MAX+ 395 combines 16 Zen 5 CPU cores, 32 threads, a 3GHz base clock, boost speeds up to 5.1GHz, 64MB of L3 cache and a Radeon 8060S integrated GPU with 40 graphics cores. AMD lists support for up to 128GB of 256-bit LPDDR5x-8000 memory, a default TDP of 55W and a configurable 45–120W range.
Because the CPU and GPU use the same memory, model weights do not have to fit inside a small, separate graphics-card VRAM pool. That makes the platform attractive for local inference. The trade-off is that the operating system, inference runtime, context cache and other applications also consume the shared pool. A machine advertised with 128GB never has the full 128GB available for model weights.
AMD’s Variable Graphics Memory feature can make up to 96GB available as graphics memory. The exact behavior depends on the mini PC’s firmware, operating system and driver settings, so the advertised maximum is a capability rather than a guaranteed allocation on every model.
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Platform specifications at a glance
| Component | Ryzen AI MAX+ 395 specification | Why it matters for LLMs |
|---|---|---|
| CPU | 16 Zen 5 cores, 32 threads; 3GHz base, up to 5.1GHz boost | Handles preprocessing, orchestration and CPU-based portions of inference |
| Integrated GPU | Radeon 8060S, 40 graphics cores | Provides the main acceleration path used by supported local LLM runtimes |
| Memory | Up to 128GB LPDDR5x-8000 on a 256-bit interface | Determines how large a quantized model and context can remain resident |
| Graphics-memory option | Up to 96GB assignable through Variable Graphics Memory, according to AMD | Can reserve more of the unified pool for GPU workloads |
| Processor power range | 55W default TDP; configurable from 45W to 120W | Sustained performance varies with the mini PC’s cooling and power configuration |
AMD’s Ryzen AI Halo Developer Platform illustrates one high-end configuration: 128GB of LPDDR5x-8000, 256GB/s memory bandwidth, a 2TB M.2 SSD, Radeon 8060S graphics and a choice of Linux or Windows 11 at a listed 120W TDP. That is a developer platform configuration, not a specification shared by every retail mini PC.
What size language model can it run?
There is no single model-size limit. “70B,” “120B” or another parameter count describes the number of learned parameters, but the file size and runtime requirements also depend on numerical precision, quantization format, context length and architecture.
How quantization changes the answer
Quantization stores model weights at lower numerical precision so they consume less memory. A quantized 70B model can therefore require substantially less memory than the same model in a higher-precision format. The quantization must still be supported by the chosen backend, and lower precision can change output quality or performance. Treat the model’s published file size as a starting point, not as the complete memory requirement.
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Why context length matters
When an LLM processes a conversation, documents or a long prompt, it maintains a key-value (KV) cache. That cache grows with context length and occupies additional unified memory while the model is running. A model that fits with a short context may fail to load, begin swapping or slow sharply when configured for a much longer context. Leave headroom for the operating system, the runtime and the KV cache instead of allocating every available gigabyte to graphics memory.
Practical model ranges
- Small and medium models: Models in the single-digit or low-double-digit billions of parameters are generally the easiest to run, with more room for long context and other applications. Exact speed still depends on quantization and backend.
- 27B-class models: AMD reported that a 64GB Radeon 8060S system, an ASUS ROG Flow Z13, ran Gemma 3 27B Vision. This demonstrates that model format and memory size matter more than the processor name alone.
- 70B-class models: A 128GB Strix Halo configuration is a credible local-inference platform for quantized 70B workloads. AMD’s 2026 test set included DeepSeek R1 Distill 70B.
- 120B-class models: AMD’s same 2026 article included GPT-OSS 120B in its tests. This establishes a demonstrated workload in AMD’s stated configuration, not universal compatibility with every 120B file, context length or runtime.
Can it run a 70B model?
Yes, with qualifications. A 128GB configuration gives a 70B quantized model substantially more working room than a 32GB or 64GB machine, and AMD has published 70B results on a 128GB Radeon 8060S setup. Before downloading a model, check the exact quantized artifact, its file size, the runtime’s overhead and the context length you intend to use.
A 64GB system is a different proposition. It can run smaller quantized models and, as AMD’s Gemma 3 27B Vision example shows, some larger models, but it leaves less room for long context, multimodal components or simultaneous applications. Two mini PCs carrying the same processor name can therefore have very different practical limits when one has 64GB and the other 128GB.
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How fast will generation be?
Performance figures are meaningful only when their test conditions travel with them. Prompt processing (reading the input) and generation (producing output tokens) are separate workloads. A benchmark that reports one without the other cannot predict every chat experience.
Published figures and their scope
| Figure | Source and configuration | How to interpret it |
|---|---|---|
| Up to 2.2× token throughput | AMD’s 2025 comparison of a 64GB Radeon 8060S ASUS ROG Flow Z13 against an Intel Core Ultra 258V laptop on selected models capped at 16GB | A company result for the listed models and workloads, not a universal mini-PC speed guarantee |
| Average 1.7× more tokens per dollar | AMD’s 2026 comparison across four large models, using LM Studio with llama.cpp on Ubuntu 24.03.3 and a 128GB Radeon 8060S configuration versus AMD’s selected 128GB NVIDIA DGX Spark setup | A vendor-published value comparison; prices, model artifacts and test settings determine the result |
| 6.23 tokens per second | Beelink’s stated test for LM Studio Qwen 32B Q8 on a Ryzen AI Max+ 395, Radeon 8060S, 128GB LPDDR5X-8000 and 2TB PCIe 4.0 SSD system | Use only as Beelink’s result for that exact model, quantization and configuration |
Do not combine these numbers into one ranking. A useful comparison records the model artifact and quantization, prompt and generation lengths, operating system, driver and runtime versions, power mode, thermal state and whether the result is prompt or generation throughput.
Which software route should you use?
AMD’s Ryzen AI Software 1.8 documentation lists Strix Halo support and identifies llama.cpp as a deployment option for the integrated GPU. AMD also documents its OGA and Lemonade software stack. Support is version-sensitive: an application may recognize the processor while lacking an optimized backend for a particular quantization or GPU path.
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The cited AMD performance work uses the Radeon GPU route. Do not assume the NPU is automatically the fastest option for general-purpose LLM inference; verify that the application and model support it before choosing that path.
A reproducible Linux-oriented setup
The following is a methodology rather than a universal one-click recipe. An independently maintained Strix Halo guide records Ubuntu 25.10, kernel 6.17, ROCm 7.2, a specific llama.cpp build and llama-bench runs with 512 prompt tokens, 128 generation tokens and three repetitions. Its memory-allocation parameters are tied to that software and hardware combination.
- Install the operating system and updates. Choose a current Windows 11 or Linux installation supported by your mini PC vendor. For Linux benchmarking, record the distribution and kernel exactly.
- Install the graphics stack. Use the ROCm and AMD runtime versions supported by your chosen llama.cpp build. Record the driver and runtime versions before testing.
- Choose a backend. Build or install a llama.cpp release with the Radeon/ROCm path enabled, or use an application such as LM Studio only when its current release documents support for your configuration.
- Set memory allocation conservatively. Reserve enough unified memory for the operating system and KV cache. Increase the graphics allocation only after confirming that the system remains stable.
- Download one known model artifact. Record the model family, quantization, file size and context setting. Do not compare a Q4 file with a Q8 file as if they were the same workload.
- Run a short functional test. Load the model, issue a prompt and watch memory usage, temperature and whether the runtime reports GPU offload.
- Benchmark separately. Measure prompt processing and generated tokens with fixed prompt and generation lengths, then repeat after changing only one variable such as context or power mode.
Choosing between 64GB and 128GB
| Decision factor | 64GB configuration | 128GB configuration |
|---|---|---|
| Model headroom | Better suited to small-to-large-but-not-extreme quantized models; 27B-class use is demonstrated on a 64GB 8060S system | More practical room for quantized 70B models and selected larger workloads |
| Long context | More likely to require compromises as KV cache grows | More headroom for long context, multimodal components or concurrent services |
| Graphics allocation | Lower total pool, so reserving memory for the GPU leaves less for the system | AMD says up to 96GB can be assigned as graphics memory, subject to platform settings |
| Upgrade path | LPDDR5x is typically soldered; verify the exact mini PC because memory expansion is not assumed | Buy the capacity you need at purchase time for the same reason |
| Value | Can be sensible if your target models are below the 70B class | Costs more but makes memory capacity the platform’s strongest advantage |
What to check before buying a Ryzen AI MAX+ 395 mini PC
- Exact memory capacity and allocation: Confirm 64GB or 128GB, firmware options and how much memory the vendor allows the GPU to reserve.
- Cooling and sustained power: AMD’s processor range reaches 120W, but each chassis may impose a lower sustained limit. Ask whether performance figures are short bursts or long runs and check fan-noise behavior.
- Storage: Large model files consume substantial SSD space. Verify the included capacity, M.2 slots and whether a second drive can be installed.
- Operating-system support: Confirm that the vendor supports the Linux distribution or Windows edition you plan to use and that graphics drivers are available.
- Ports and networking: Local inference does not require an internet connection after model installation, but fast networking helps with downloading models and serving requests to other devices.
- Warranty and regional terms: Integrated memory is not a user-upgradeable safety net on these systems, so return policy and warranty support matter.
- Comparable testing: Require the model name, quantization, context, backend, operating system and power profile before treating a vendor token-rate claim as predictive.
A real 128GB example
Beelink lists a GTR9 Pro configuration with the Ryzen AI Max+ 395, Radeon 8060S, 128GB LPDDR5X-8000 and a 2TB SSD. Its page also reports the Qwen 32B Q8 result shown above. Treat that listing as evidence that the configuration exists, not as a guarantee of current stock, price, independent performance or support in your region. Verify the exact SKU, seller, warranty, return policy and memory specification before ordering.
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Common failure modes and fixes
The model will not load
Check the quantized file size, context setting and available system memory. Reduce context, close other applications, lower the graphics reservation if it is unnecessarily large, or select a smaller quantization. If the model still exceeds available memory, a different runtime cannot create capacity that the hardware does not have.
The model loads but is unexpectedly slow
Confirm that GPU offload is active and that the runtime is using a supported ROCm or Radeon backend rather than silently falling back to the CPU. Then check power mode, sustained temperature and fan behavior. Compare generation tokens per second separately from prompt-processing speed.
Results change between applications
Different applications may use different llama.cpp revisions, kernels, drivers, quantization support and memory-allocation defaults. Record those versions and reproduce the same model and context before concluding that one application or mini PC is faster.
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The KV cache grows as context grows. Lower the context limit, start a new conversation, reduce concurrent workloads or use a model configuration that leaves more memory headroom.
Bottom line
A 128GB Ryzen AI MAX+ 395 mini PC is a capable local-LLM platform for users who value memory capacity and an integrated, compact system. Quantized 70B models are a defensible target, and AMD has published tests that include a 120B model. The purchase decision should be based on the exact memory configuration, model artifact, context length, backend, cooling and power behavior—not on the processor name or an isolated tokens-per-second claim.
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