Choose the Ryzen AI MAX+ 395 when fitting a larger model is the priority; choose an RTX 4090 when a 24 GB model fits and you want the most established discrete-GPU software path. AMD describes Ryzen AI Max+ 395 systems with up to 128 GB of unified memory and up to 96 GB assignable as graphics memory through Variable Graphics Memory. NVIDIA specifies 24 GB of GDDR6X on the RTX 4090. That capacity difference can determine whether a quantized model loads at all, but it does not prove that the AMD system generates tokens faster.
No independent, controlled, like-for-like benchmark between these platforms was established. AMD’s published comparison is a vendor result on one LM Studio workload, while its other throughput figure uses a different model and benchmark. Treat those figures as configuration-specific evidence, then test the exact model, context length, quantization and backend you plan to use.
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The decision in one table
| Question | Ryzen AI MAX+ 395 system | RTX 4090 system |
|---|---|---|
| Maximum memory advertised for the platform | Up to 128 GB unified memory; AMD says up to 96 GB can be configured as graphics memory with Variable Graphics Memory (AMD, 2025). | 24 GB GDDR6X dedicated VRAM (NVIDIA product specification, accessed 2026). |
| Can capacity exceed 24 GB? | Yes, on systems fitted with enough memory and configured to give the GPU a large allocation. Actual free space is lower after the operating system and runtime reserve memory. | No; model weights, KV cache and runtime allocations must fit within the 24 GB card or be partly offloaded elsewhere. |
| Published performance evidence | AMD reports up to 61 tokens per second on Phi-3.5 with MLPerf Client v1.0. AMD also shows a vendor comparison on Llama 3.1 70B Q4 in LM Studio. | NVIDIA documents a 19 GB model that can be fully GPU-accelerated on a 24 GB RTX 4090 in an LM Studio example. |
| Best first question | Can the specific computer provide enough usable unified memory and a supported ROCm or other backend? | Does the quantized model plus context and KV cache stay below roughly 24 GB, and does the chosen runtime use CUDA correctly? |
| Evidence limitation | The figures above are not directly comparable scores: they use different models, software, memory arrangements and measurement methods. | |
For a model that fits comfortably in 24 GB, an RTX 4090 is usually the simpler performance choice because it is a mature discrete-GPU target for CUDA-based local-inference software. That is a practical software-path judgment, not a claim that every workload is faster. When a model or context does not fit, the 395’s larger memory pool can be decisive.
Why memory capacity comes before token speed
Weights are only the beginning
Local inference must hold the model’s quantized weights, temporary working buffers, attention data and the key-value (KV) cache used for the conversation context. Increasing context length increases KV-cache consumption, so a model that loads with a short prompt can fail when you raise the context window. The runtime also needs headroom for operating-system allocations and its own kernels.
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Consequently, “24 GB card” and “24 GB available to the model” are not identical. Likewise, a 128 GB computer does not expose all 128 GB to inference: the OS, applications and runtime consume part of it, and the machine’s firmware or memory policy may reserve a portion for graphics. Record the amount the runtime actually reports before drawing a capacity conclusion.
Discrete VRAM versus unified memory
The RTX 4090 has a dedicated 24 GB GDDR6X pool attached to the GPU. The Ryzen AI MAX+ 395 uses unified memory shared by the CPU and graphics resources. AMD’s Variable Graphics Memory setting can assign up to 96 GB as graphics memory on a suitably configured system with up to 128 GB total. Unified memory can make a larger model possible, but CPU and GPU activity compete for the same physical pool, and bandwidth and allocation policy differ from a discrete card.
Do not translate “96 GB graphics memory” into “96 GB of RTX-class VRAM.” It is a configurable limit inside a particular system design. Cooling, memory speed, firmware settings and the selected graphics allocation affect usable performance. A 64 GB graphics allocation in one 128 GB computer is not evidence that every Ryzen AI MAX+ 395 notebook or mini-PC behaves identically.
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NVIDIA’s LM Studio article describes a model requiring 19 GB of VRAM that can be fully accelerated on a 24 GB RTX 4090. The example illustrates the fit test: leave room for the KV cache and runtime overhead rather than treating the nominal model size as the entire requirement. A larger quantization, longer context or a different implementation can push the same family of model beyond the card’s practical limit.
Can the Ryzen AI MAX+ 395 run a 70B model locally?
It can be a viable platform for a 70-billion-parameter model when the computer has enough total memory, the quantized file and context fit in the graphics allocation, and the selected backend supports the hardware. AMD’s CES 2025 material specifically used Llama 3.1 70B Q4 in an LM Studio comparison, demonstrating that AMD is targeting this class of workload.
That demonstration does not guarantee a particular 70B file will run on every 395 system. Quantization formats differ, context length changes KV-cache size, and an OEM may ship less memory or configure a smaller graphics allocation. Before buying, check the exact computer’s total memory, the maximum graphics-memory setting, and the runtime’s documented support for the model format.
An RTX 4090 can run a 70B model only when the chosen quantization and context fit its 24 GB VRAM or when the runtime accepts partial CPU offload. Offload can allow execution, but moving layers or cache data between system memory and the card changes latency and throughput. A model that technically starts is not necessarily pleasant for interactive use.
What the published performance numbers actually show
AMD’s LM Studio comparison
AMD’s CES 2025 presentation compares a Ryzen AI Max+ 395 and an RTX 4090 on an LM Studio Llama 3.1 70B Q4 workload. The presentation is vendor-produced. The surfaced evidence does not establish identical context length, quantization files, LM Studio version, memory allocation, power settings or measurement procedure, so its relative result cannot be generalized to every model. If you cite the comparison, identify the exact Llama 3.1 70B Q4 and LM Studio setup and label the result as AMD-reported.
Do not turn a vendor’s “2.2×” style comparison into a universal ranking. A result on one large quantized model may reflect memory capacity, a particular kernel implementation or a configuration that is unavailable on another 395 computer. It says little about a small model, long-context prompt processing or a different operating system.
AMD’s Phi-3.5 result
AMD Developer Resources reports up to 61 tokens per second on Phi-3.5 using MLPerf Client v1.0. “Up to” describes the reported best case, and the model and benchmark are part of the number’s meaning. It is not an RTX 4090 head-to-head result and should not be compared directly with a tokens-per-second figure measured on another model or client.
Why one TPS number cannot answer the buying question
Token generation and prompt processing stress different parts of the stack. A short prompt with a small batch can produce a different rate from a long-context conversation. Quantization, sampling settings, batch size, context length, thermal limits and whether every layer is on the GPU all change the result. Measure both prompt-processing speed and generation speed if your workload includes document ingestion or codebase indexing.
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Software and operating-system fit
Start with the backend, not the logo
NVIDIA’s guidance is to choose a backend after checking the operating system, model format, GPU architecture, available memory, API requirements and target throughput. Apply that checklist to both platforms. A runtime may recognize a device yet leave layers on the CPU, use a fallback kernel or support only selected quantization formats.
For an RTX 4090, verify that the runtime is using the CUDA device rather than an integrated or software backend. For a Ryzen AI MAX+ 395 system, verify the current ROCm support for your operating system and application; support can vary by release and distribution. If your preferred application has stronger CUDA documentation than AMD support, that software advantage may outweigh the 395’s capacity.
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A documented AMD Linux configuration
AMD’s ROCm article documents one 128 GB Ryzen AI Max+ 395 system with 64 GB allocated to the GPU, Ubuntu 24.04 LTS, ROCm 7.2.1 and Ollama 0.20.x. Those versions and allocations describe that example, not a universal requirement or guarantee. Software releases change, and an OEM’s firmware may expose different memory controls.
Model format and API checks
- Confirm the exact quantized file format accepted by the runtime you intend to use.
- Check whether GPU offload is automatic or requires an explicit layer or memory setting.
- Verify that the application’s API mode, tool-calling support and context-window limit match your use case.
- Record runtime and driver versions so a later update can be compared against a known-good configuration.
A repeatable setup and test procedure
- Define the workload. Write down the model name, quantization, maximum context, expected batch size, prompt length and whether you need interactive chat, embeddings or batch generation.
- Measure available memory. On the RTX system, inspect the 4090’s free VRAM after the desktop and runtime start. On the 395 system, record total memory and the graphics-memory allocation exposed by firmware or the vendor utility. Leave headroom for the KV cache and operating system.
- Install a supported backend. Use the runtime’s current instructions for your operating system and GPU architecture. Check the installed driver or ROCm version, then confirm that the application reports the intended device.
- Load a small known model first. Confirm that the model is actually GPU-accelerated and that generation works before testing the large model. A successful launch with CPU fallback is not a valid GPU comparison.
- Load the target quantization. Start with a conservative context length. Record peak memory, prompt-processing rate, generation rate and whether any layers are offloaded to the CPU.
- Increase context deliberately. Repeat at the context sizes you will use in practice. Stop when memory pressure, swapping or allocation errors appear; do not treat an unstable maximum as a usable setting.
- Repeat runs. Warm up the model, use the same prompts and sampling settings on both systems, and run enough repetitions to avoid quoting a single anomalous result.
- Save the configuration. Keep the model checksum or file identifier, runtime version, driver or ROCm version, memory allocation, power mode and measured rates with your notes.
How to compare the two systems fairly
| Variable | Hold constant | Why it matters |
|---|---|---|
| Model and quantization | Use the same model file and quantization level. | Different quantizers change both memory use and arithmetic workload. |
| Context and prompt | Use identical prompt text and context length. | KV-cache size and prompt-processing work rise with context. |
| Runtime | Use the same application release where both backends support it, or document the difference. | Kernel and scheduler implementations can dominate results. |
| GPU residency | Record layers on the GPU and any CPU offload. | A nominally identical test can become a different workload when data crosses the bus. |
| Power and thermals | Use comparable performance modes and run after warm-up. | Short bursts can hide sustained throttling. |
| Metrics | Report prompt-processing and generation tokens per second separately, plus peak memory. | One aggregate TPS value conceals interactive and batch behavior. |
If you cannot keep these variables constant, present the results as separate system demonstrations rather than a winner-takes-all benchmark.
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You need the largest practical model on one machine
Favor a 395 computer with a high-memory configuration, provided the exact system lets you assign enough memory to graphics and your backend supports the model. The advantage is the larger addressable pool, not a guaranteed token rate. Confirm that the manufacturer does not cap graphics allocation below what your model needs.
You run models that fit under 24 GB
The RTX 4090 is the straightforward candidate when the model, context and runtime overhead fit. Its discrete VRAM avoids sharing the pool with ordinary CPU applications, and CUDA support is a familiar target for many inference tools. Still benchmark your chosen application; software quality matters more than a specification-sheet assumption.
You change models frequently
Estimate the largest quantized model and context you expect over the system’s useful life, then add headroom. A 24 GB limit can force a lower quantization or CPU offload later. A 128 GB unified-memory configuration offers more room, but memory is part of the complete computer and cannot necessarily be upgraded after purchase.
You need predictable, sustained throughput
Prioritize measured results from the exact chassis, cooling solution and power configuration. Matched power draw, noise, prices or sustained throughput for the two platforms are not established, so those factors require product-specific verification before purchase.
You depend on a specific API or application
Choose the platform with confirmed support for that application’s model format, API mode and GPU acceleration. Verify release notes and run a small model before committing to a high-memory configuration; capacity is wasted if the software falls back to the CPU.
Common failure modes and fixes
Out-of-memory errors on the RTX 4090
- Reduce context length and retest, because KV-cache allocation may be the immediate cause.
- Use a smaller or more aggressively quantized model only if its quality is acceptable.
- Disable unrelated GPU applications and confirm that the runtime is not reserving memory for another process.
- Use CPU offload as a compatibility fallback, then measure whether the resulting latency meets your needs.
Large memory reported but model still fails on the 395
- Check the graphics-memory allocation rather than total system RAM.
- Confirm that the runtime recognizes the intended accelerator and is not constrained by a smaller per-process limit.
- Leave room for the OS, runtime buffers and KV cache; configured capacity is not free capacity.
- Test the model with a shorter context to distinguish weight capacity from cache growth.
The application starts but runs slowly
- Inspect the log for CPU fallback or partial layer offload.
- Check driver or ROCm compatibility with the runtime release.
- Repeat after warm-up and monitor sustained clocks and temperature.
- Compare prompt and generation rates separately; a slow prompt phase does not necessarily mean generation is equally slow.
Results change after an update
Keep a record of model file, runtime, driver or ROCm version, memory allocation and power mode. Re-run the same prompts after updates. A changed kernel or allocator can alter both memory use and throughput without any hardware change.
Cost, power and upgrade considerations
Current prices, power draw under matched inference workloads, system availability and upgrade paths were not verified for this comparison. Do not infer them from the processor or GPU name. Compare complete systems using current product specifications: purchase price, memory capacity, power supply, cooling, noise, warranty and whether memory is replaceable.
The RTX 4090 is a component, so the final system also needs a suitable CPU, motherboard, power supply, cooling and chassis. A Ryzen AI MAX+ 395 purchase is usually a complete compact system or notebook, making the memory configuration and thermal design inseparable from the processor choice. Ask the seller for the exact total memory and graphics allocation rather than relying on the platform label.
A practical buying checklist
- Model fit: calculate weight, KV-cache and runtime headroom for your largest planned context.
- Actual memory: verify free VRAM on the 4090 or graphics allocation and free unified memory on the 395 system.
- Backend: confirm operating-system, driver or ROCm, model-format and API support for your application.
- Performance: find a benchmark using the same model and context, or plan your own controlled test.
- Sustained behavior: check cooling, power mode and long-run throttling on the exact chassis.
- Longevity: decide whether future models are more likely to exceed 24 GB or whether software compatibility is the bigger risk.
- Verification: obtain the seller’s precise memory configuration and return policy before purchase.
Bottom line
The Ryzen AI MAX+ 395 and RTX 4090 solve different constraints. The 395’s up-to-128 GB unified-memory configurations, with up to 96 GB assignable to graphics, can make large quantized models and longer contexts practical when a 24 GB card cannot load them. The RTX 4090’s 24 GB GDDR6X is a hard, easy-to-understand limit but remains a strong choice for models that fit and for software stacks optimized around a discrete CUDA GPU.
Neither platform has a universally proven speed advantage from the available evidence. AMD’s LM Studio Llama 3.1 70B Q4 comparison and its up-to-61-tokens-per-second Phi-3.5 result are workload-specific vendor figures. Select the platform that fits your model first, verify the backend on your operating system, and compare sustained prompt and generation rates on the exact configuration you will use.
Quick Recap
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