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Choose the Ryzen AI MAX+ 395 when fitting a larger model is the priority; choose an RTX 4090 when your model fits in its dedicated VRAM and you want the most established discrete-GPU software path. The 395 can expose a much larger pool of unified memory, while the 4090 offers a fixed, high-bandwidth graphics-memory pool. Capacity determines whether a model loads at all; it does not, by itself, prove which system generates tokens faster.
No independent, controlled, like-for-like benchmark between these platforms was established. AMD’s published comparison and throughput figures are useful evidence for particular workloads, but they should not be treated as a universal speed ranking.
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At a glance: what separates the two platforms
| Criterion | Ryzen AI MAX+ 395 system | GeForce RTX 4090 system | Why it matters for local LLMs |
|---|---|---|---|
| Memory architecture | Unified system memory shared by CPU and GPU | Dedicated discrete GPU memory | Unified memory can be assigned flexibly, while discrete VRAM is isolated but predictable. |
| Maximum configuration cited by the vendors | Up to 128 GB total unified memory; up to 96 GB can be configured as graphics memory through Variable Graphics Memory (AMD, 2025) | 24 GB GDDR6X (NVIDIA product specification, accessed 2026) | The larger 395 configuration can accommodate models that cannot fit wholly in the 4090’s VRAM. |
| Reference model-fit example | AMD presents the platform for large-model local inference; exact fit depends on the computer’s installed memory and allocation | NVIDIA’s LM Studio example fully accelerates a model requiring 19 GB of VRAM on a 4090 (2024 article) | Weights, context and runtime overhead must all fit in the memory available to the backend. |
| Published throughput figure | Up to 61 tokens per second on Phi-3.5 with MLPerf Client v1.0 (AMD Developer Resources, 2025) | No directly comparable figure was established in the supplied material | A token-per-second result is meaningful only with the same model, quantization, context, software and measurement method. |
| Documented software example | Ubuntu 24.04 LTS, ROCm 7.2.1 and Ollama 0.20.x on a 128 GB system with 64 GB allocated to the GPU (AMD, current example) | Backend choice depends on operating system, model format, GPU architecture, memory, API needs and throughput target (NVIDIA guidance) | Actual support and performance vary with the operating system, runtime version and model format. |
The table compares specifications and published examples, not matched benchmark scores. A configured memory maximum is also not the same as memory available to your model after the operating system, desktop, runtime and cache have taken their share.
Model fit comes before speed
Inference is a two-stage decision. First determine whether the complete workload fits; only then compare how quickly each system processes it. “The model fits” means more than loading its weight file. The backend must reserve space for the quantized weights, the requested context window, the key-value (KV) cache that grows as a conversation expands, temporary tensors and its own runtime overhead.
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Why the 4090 can be an excellent fit
A model whose total working set stays below the card’s available VRAM can run entirely on the GPU. NVIDIA’s LM Studio article gives a concrete example: a cited model requiring 19 GB of VRAM is fully GPU-accelerated on a 4090. Full offload avoids moving layers between system memory and the card during generation, which is generally the simplest configuration to tune.
That example is not a promise that every similarly named model will fit. Different quantization files, context lengths and runtime versions change the working-set requirement. Leave headroom rather than treating the specification as a target allocation; a model that loads with a short prompt can still run out of memory when the KV cache grows.
Why the 395 changes the capacity calculation
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. CPU and GPU draw from the same physical pool, so the number you can allocate to graphics is not automatically free for model weights. The operating system, applications and inference runtime still need memory.
This architecture can make a model practical when a 24-GB discrete card cannot hold the required weights and cache. It does not turn the entire configured pool into dedicated high-speed VRAM, and it does not establish a particular generation rate. A 395 computer with less installed memory, or with a different graphics-memory allocation, will have a different fit than the maximum configuration described by AMD.
Quantization and context are part of the model choice
Use the exact quantization file you intend to run. A “70B” label does not specify the file size, and the same model family can have several quantization levels. The context setting is equally important: increasing it expands the KV cache and can push a previously successful load over the available-memory limit.
- Record the model identifier and exact quantization file.
- Set a context length that matches your application instead of accepting an unnecessarily large default.
- Check the runtime’s reported memory requirement before starting a long session.
- Test the longest prompt and response you expect, not only a short greeting.
What the published speed evidence actually shows
AMD’s CES 2025 comparison
AMD’s CES 2025 material compares a Ryzen AI MAX+ 395 system with an RTX 4090 using Llama 3.1 70B Q4 in LM Studio. This is relevant evidence that the 395 is intended to run large quantized models locally, but it is a vendor presentation rather than an independently controlled head-to-head test.
The surfaced material does not establish identical context length, model files, LM Studio versions, memory allocation, power settings or measurement method. Therefore, the slide cannot prove that the 395 is generally faster than a 4090, nor can a result from that workload be transferred to another model.
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AMD Developer Resources reports up to 61 tokens per second on Phi-3.5 using MLPerf Client v1.0. “Up to” describes the best reported result under that benchmark’s conditions; it is not an RTX 4090 comparison and should not be used as one. Phi-3.5 also represents a different model and workload from Llama 3.1 70B Q4, so the figures answer different questions.
Why one token-per-second number is insufficient
Generation speed and prompt-processing speed are separate. A system may decode responses quickly but take longer to ingest a very long prompt, or show a strong first-token time while settling to a lower sustained rate. Measure both phases with the same script and stopping conditions.
Report the backend, runtime version, quantization, context length, prompt and output token counts, GPU-offload setting, memory allocation and power mode with every result. Without those details, a number is a marketing data point rather than a reproducible comparison.
Software support can outweigh the hardware specification
NVIDIA’s guidance is to select a backend after checking the operating system, model format, GPU architecture, available memory, API requirements and desired throughput. Apply the same checklist to the 395. A model that is theoretically small enough may still fall back to CPU, use an unsupported kernel or fail to load if the chosen backend does not support that GPU path.
AMD’s documented Linux stack
AMD documents one current example with Ubuntu 24.04 LTS, ROCm 7.2.1 and Ollama 0.20.x. The computer has 128 GB of system memory, with 64 GB allocated to the GPU. Treat those versions and the allocation as a reproducible reference configuration, not as a requirement or guarantee for every 395 computer. Software releases, kernel support and performance can change.
Before installing, verify that the exact computer model exposes the required graphics-memory controls and that the ROCm release supports its integrated GPU. Then confirm in the runtime’s diagnostic output that the GPU backend is active and that the expected amount of memory is visible.
Using a 4090 with a local runtime
A discrete 4090 generally gives a backend a clearly defined graphics-memory device, but the application still needs a compatible driver and runtime. Confirm that your selected application supports the installed driver, the model format you downloaded and the APIs you intend to use. In LM Studio or another frontend, inspect the device and offload report rather than assuming that selecting a GPU resulted in full acceleration.
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Frontend settings that affect both systems
- GPU offload: Prefer full offload when the working set fits. If it does not, record how many layers are on the GPU and how many remain on the CPU.
- Context length: Keep it fixed between tests; changing it changes KV-cache memory and speed.
- Batch or prompt-processing settings: Keep them identical when comparing prompt ingestion.
- Threads and CPU affinity: Record these when any portion of the model runs on the CPU.
- Quantization format: Use the same file format and quantization level on both platforms whenever compatible.
How to run a fair comparison at home
The following procedure produces a useful personal result without pretending to be a laboratory benchmark. Run it only after confirming that each platform supports the exact model file.
- Choose one workload. Select a named model and exact quantization. For a large-model test, document whether you are reproducing the Llama 3.1 70B Q4 workload used in AMD’s presentation or running a different file.
- Fix the context. Choose one context length and use it on both systems. Prepare a prompt set with a short prompt and a long prompt representative of your application.
- Start from a clean state. Close other GPU applications, reboot if necessary, and allow each runtime to finish loading before timing generation. Note the operating-system version and runtime version.
- Verify memory before timing. Record total memory, memory assigned to graphics on the 395, available VRAM on the 4090, and the runtime’s allocation after the model loads.
- Verify acceleration. Capture the backend diagnostic. Record full GPU offload, partial offload or CPU execution; do not compare a fully accelerated run with a CPU-heavy run.
- Warm up the model. Generate one untimed response so compilation and initial kernel setup do not dominate the result.
- Measure prompt processing. Use the same prompt and record time to first token or the runtime’s prompt-processing rate. Repeat at least three times and keep the same prompt order.
- Measure generation. Generate a fixed number of output tokens at a fixed sampling configuration. Record sustained tokens per second after the first-token delay.
- Repeat the long-context case. Extend the prompt to the planned context length. Watch memory use and note whether the model slows, spills to CPU or fails.
- Publish the conditions with the result. Include model file, quantization, context, prompt and output lengths, backend, versions, allocation and power mode. If a run fails, report the failure instead of silently changing settings.
| Metric to record | What it tells you | Common misreading |
|---|---|---|
| Load success and load time | Whether the working set fits and how long startup takes | Assuming a successful load means the longest context will also fit |
| Prompt-processing rate | How quickly the system ingests input tokens | Calling this the response-generation speed |
| Time to first token | Interactive responsiveness after submitting a prompt | Comparing runs with different prompt lengths |
| Sustained generation rate | Decode performance for a continuing response | Using a short burst or warm-up result as a long-run rate |
| Peak memory and allocation | Whether context growth leaves safe headroom | Treating installed or configured memory as entirely available to the model |
| Offload pattern | How much work is actually on the GPU | Assuming the application selected a GPU without checking diagnostics |
Which platform suits common local-inference goals?
You need the largest practical model
Start with the 395 if your chosen model and context cannot fit in the 4090’s dedicated memory. Confirm the exact computer’s installed capacity and graphics-memory allocation; “up to” specifications describe a maximum configuration, not every retail system. Expect to tune context and leave room for the operating system.
Your model fits comfortably and you value a conventional GPU stack
Start with the 4090 when the complete working set fits in its VRAM and your preferred application has a tested backend for it. Its fixed memory boundary makes capacity planning straightforward. You still need to verify driver, model-format and runtime compatibility.
You care about predictable interactive latency
Benchmark the exact conversation pattern. Short prompts, long documents and long generated answers stress different parts of the system. A capacity advantage does not automatically yield lower first-token latency, and a high decode rate on one model does not predict another.
You want to run several models or services
Measure cumulative memory use, not only the largest single model. Keeping a second model, embedding service or desktop application resident reduces the memory available for inference. The unified design may offer more total room, while the discrete card offers a clear per-device limit; your orchestrator must support the way you intend to share that memory.
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Processor-level specifications do not guarantee identical computer behavior. Before purchasing a 395 system, obtain the exact memory capacity, the available Variable Graphics Memory setting, cooling design, operating-system support and upgrade options. Before purchasing a 4090 system, verify the card’s physical fit, power delivery, cooling and the memory available after the driver and desktop are loaded.
Current prices, power draw under matched inference workloads, noise, cooling performance, system availability and upgrade paths were not established in the published material. Compare those items using current specifications for the exact computer or card rather than extrapolating from the processor or GPU name.
Capacity checklist for a Ryzen AI MAX+ 395 computer
- Installed unified-memory capacity, not only the processor family name.
- Graphics-memory allocation available in the system firmware or vendor utility.
- Operating-system and ROCm support for the exact release you plan to use.
- Headroom for the operating system, desktop and other services.
- Cooling that can sustain a long generation session without reducing clocks.
Capacity checklist for an RTX 4090 computer
- Available VRAM after the driver and inference application start.
- Driver and backend support for the model format and GPU architecture.
- Power supply, cooling and case clearance for the installed card.
- Whether the workload needs CPU offload when context grows.
- Headroom for other GPU applications that may run concurrently.
Troubleshooting model-load and performance problems
The model does not load
Check the runtime’s memory estimate against available memory, then reduce context length and close other applications. If the model still exceeds the limit, use a smaller quantization or a model with fewer parameters. On the 395, verify the actual graphics-memory allocation; on the 4090, remember that the card’s dedicated pool cannot be expanded by installing more system RAM.
The model loads but generation is unexpectedly slow
Inspect the backend report for CPU fallback or partial offload. Confirm that the intended GPU is selected, the model file is supported and the runtime is using the expected acceleration library. Repeat after a warm-up and record whether the slowdown appears during prompt processing, first-token delay or sustained generation.
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Long conversations fail after short tests succeed
The KV cache grows with context. Lower the context limit, summarize older turns or choose a quantization that leaves more headroom. Test the longest realistic conversation before relying on a configuration for production use.
Results change after a software update
Record the operating system, driver, ROCm or other acceleration-library version, frontend version and model file. Re-run the same fixed workload after an update; do not combine results from different stacks into one ranking.
Decision framework
| If your priority is… | Lean toward… | Validate before committing |
|---|---|---|
| Running a model whose working set exceeds the 4090’s VRAM | A high-memory Ryzen AI MAX+ 395 configuration | Installed memory, graphics allocation, backend support and long-context stability |
| Full GPU acceleration for a model already within the 4090’s limit | RTX 4090 | Driver, runtime, model format and sustained cooling |
| AMD’s published large-model software example | 395 with the documented Linux stack | Ubuntu 24.04 LTS, ROCm 7.2.1, Ollama 0.20.x compatibility on your exact computer |
| Highest confidence in a speed claim | Neither by specification alone | Run the same model, quantization, context and measurement procedure on both |
| Lowest total ownership cost or noise | Whichever complete system wins your current comparison | Current price, power, cooling, acoustics and availability for specific products |
Bottom line
The Ryzen AI MAX+ 395 and RTX 4090 solve different constraints. The 395’s large, configurable unified-memory pool can make larger local models feasible, but capacity is not a speed guarantee and varies by system configuration. The 4090’s dedicated memory offers a clear, conventional GPU path and can fully accelerate models that fit within it, as NVIDIA’s 19-GB LM Studio example illustrates.
For a defensible choice, identify the exact model file and context first, verify that each platform can keep the workload on its intended accelerator, and benchmark prompt processing and generation under identical conditions. Treat AMD’s CES 2025 Llama 3.1 70B Q4 comparison and its “up to 61 tokens per second” Phi-3.5 result as workload-specific vendor evidence—not a universal ranking.
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