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AMD Strix Halo vs NVIDIA RTX 4090: What to Choose for Local LLMs in 2026

Updated: 26.10.2026 · AI Release · @ai_release1

TL;DR

Time: 60–90 minutes for one benchmark pass on each machine, plus model download time.
Difficulty: Intermediate.
You need: both platforms, one GGUF model file, one fixed prompt, llama-cli or another runner that reports tokens/s, nvidia-smi for RTX 4090, and the AMD monitoring tool documented for Strix Halo.
Decision rule: choose the platform that fits your required model and context at an acceptable tokens/s rate; do not choose by brand name alone.
Important: the source posts for this guide do not publish benchmark numbers or prices for these platforms. The numbers below are examples of the measurement format, not hardware claims.

Requirements and setup

Two test machines: one AMD Strix Halo platform and one NVIDIA RTX 4090 machine. Use the same OS family and the same inference runner where possible; if you cannot, record the difference in the results table.
A GGUF model file and a fixed prompt. Example model path: ./models/<model>.gguf. Replace <model> with the file you actually downloaded.
An inference runner that prints prompt-eval and eval timings. The examples use llama-cli; check llama-cli --help for your build.
For RTX 4090: a CUDA-enabled build and nvidia-smi. Verify with:

nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv

Expected: a header plus one row with the GPU name, a non-zero memory total, and a driver version. If the command is not found, install the NVIDIA driver tools first.

For Strix Halo: the AMD GPU runtime and backend documented for your platform. Follow the AMD setup in How to Run a Local LLM Server on AMD Strix Halo: HALOGEN and Qwen on Fedora 43. Verify with the monitoring tool named in that guide; rocm-smi or radeontop are common examples, but use what your platform documents.
Enough free disk space for the model file plus logs. Check the model card for the file size; do not guess.
Optional: a wall power meter. Software GPU power readings are useful, but they do not capture whole-system idle and load power.

Example: if your model card lists a 5 GB file for 4-bit quantization, write 5 in the budget calculator in Step 1. If the card does not list a context-cache size, check the runner documentation for the KV-cache formula.

Step 1. Define the model memory budget

What to do: calculate the minimum memory your target model needs before you install or buy anything. Use the model file size, the context cache, and a runtime overhead allowance.

python3 - <<'PY'
model_gb = float(input("model file GB: "))
ctx_gb = float(input("context cache GB: "))
runtime_gb = 2.0
print(f"minimum budget: {model_gb + ctx_gb + runtime_gb:.1f} GB")
PY

Example:

model file GB: 5
context cache GB: 2
minimum budget: 9.0 GB

Why: the model weights and KV cache are the two largest fixed allocations. The runtime allowance covers temporary buffers, graph memory, and fragmentation. If the budget is close to the usable memory reported by the OS or GPU, expect an out-of-memory failure at longer context.

How to check: compare the result with the memory the platform actually reports.

free -g

Expected on a Linux machine: a Mem: row with total, used, free, and available columns. On RTX 4090, also run:

nvidia-smi --query-gpu=memory.total,memory.used --format=csv

Expected: a header and one row with non-zero memory.total and memory.used. If available or free VRAM is below your budget, reduce context, quantization, or offloaded layers before benchmarking.

Step 2. Run the same benchmark on both platforms

What to do: run one fixed prompt with one fixed model on both machines. Record prompt-eval tokens/s, eval tokens/s, wall time, and peak memory.

/usr/bin/time -f "wall=%e s, maxrss=%M KB" \
  llama-cli -m ./models/<model>.gguf \
  -p "Summarize the following text in 200 words: <fixed text>" \
  -n 256 -c 4096 -ngl 99

-ngl 99 means “offload all layers to the GPU.” If the run fails with an out-of-memory error, lower it, for example -ngl 20. On AMD Strix Halo, confirm the backend flag and offload behavior in your platform documentation; the linked AMD guide is the starting point.

Example: your output should contain lines in this format:

llama_perf_context_print: prompt eval time = 1200.00 ms / 50 tokens (24.00 ms per token, 41.67 tokens per second)
llama_perf_context_print: eval time = 25000.00 ms / 256 runs (97.66 ms per token, 10.24 tokens per second)

Record 41.67 and 10.24 in the table. Your values will differ; the format is what matters.

Why: the same model and prompt isolate the platform, backend, and memory behavior. If you change the model or prompt between runs, you are comparing workloads, not hardware.

How to check: the command must exit with code 0 and print both prompt eval time and eval time. If it prints only CPU lines, the GPU backend is not active. Record the results:

Metric	AMD Strix Halo	NVIDIA RTX 4090	Pass condition
Peak memory used	`<value>`	`<value>`	Below usable memory
Prompt-eval tokens/s	`<value>`	`<value>`	Higher is better
Eval tokens/s	`<value>`	`<value>`	Above your target
Wall time for 256 tokens	`<value>`	`<value>`	Lower is better
Driver/runtime version	`<value>`	`<value>`	Matches platform docs

Option	What changes in the local LLM workflow	Price
AMD Strix Halo	You test the AMD GPU backend and watch system-wide memory; confirm the exact memory topology and usable memory in your platform documentation.	Not stated in the source posts; check current retail price.
NVIDIA RTX 4090	You test the CUDA backend and watch VRAM; when VRAM is exhausted, layers spill to system RAM or the run fails.	Not stated in the source posts; check current retail price.

If you are still choosing the model itself, use Best AI models and neural networks in 2026 before repeating this step.

Step 3. Choose the platform from your measurements

What to do: apply three rules to the table from Step 2.

1. If the model does not fit on RTX 4090 at your required context, and it fits on Strix Halo at acceptable tokens/s, choose Strix Halo.

2. If both fit and both meet your tokens/s target, choose the lower total cost of ownership: purchase price, power draw, cooling, and noise.

3. If neither meets your tokens/s target, reduce quantization, reduce context, or choose a smaller model. Do not buy a platform to fix a workload that is too large.

Example decision record:

cat > decision.txt <<'EOF'
Target: 15 tokens/s, 8K context, 4-bit model
AMD Strix Halo: fit=yes, eval=18 tokens/s
RTX 4090: fit=no at 8K, eval=12 tokens/s at 4K
Choice: AMD Strix Halo
EOF

This is an example only; replace the numbers with your measured results.

Why: a local LLM purchase fails in two ways: the model does not fit, or it fits but is too slow for the workflow. The table makes both failure modes visible before you spend money.

How to check: open decision.txt and confirm every claim points to a measured row in the Step 2 table.

cat decision.txt

Expected: a short record with a target, one fit result per platform, and one choice. If a line has no measured value, the decision is not ready.

Troubleshooting

Problem. RTX 4090 run stops with an out-of-memory error at full offload.

Error: CUDA error: out of memory
Cause: model weights, KV cache, and runtime buffers exceed VRAM at the chosen context and -ngl value.
Solution: lower offloaded layers and context, then rerun:

llama-cli -m ./models/<model>.gguf -p "<prompt>" -n 128 -c 4096 -ngl 20

If it still fails, use a smaller quantization or a smaller model.

Problem. AMD build starts but does not use the GPU.

Error: hipErrorNoBinaryForGpu: Unable to find code object for all current devices
Cause: the runtime or build target does not match the GPU reported by the driver.
Solution: check the GPU target and rebuild for it. Example check:

rocminfo | grep -i gfx

Then follow the AMD setup guide for the correct backend and build flags. If rocminfo is not installed, install the platform tools first.

Problem. The model file loads on one machine but not the other.

Error: llama_model_load: error loading model: unknown model architecture
Cause: the file is not a supported GGUF model, is truncated, or the runner build does not support that architecture.
Solution: verify the file and use a supported model:

ls -lh ./models/<model>.gguf
file ./models/<model>.gguf

If the file is incomplete, download it again. If the architecture is unsupported, convert it to GGUF with a compatible tool or choose another model.

Summary and next steps

Run the memory-budget calculation and the same benchmark on both machines, then choose the platform that meets your tokens/s target without exceeding usable memory. Next, lock the driver and runtime versions and save the benchmark command in a script so you can repeat the comparison after updates.

FAQ

Is Strix Halo better than RTX 4090 for local LLMs?

There is no universal answer. It depends on whether your model and context fit, and whether the measured tokens/s meets your target. Use the Step 2 table.

Can RTX 4090 run a 70B model?

Check the model card and run the Step 1 budget calculation. If the model file plus context cache exceeds usable VRAM, reduce quantization or offload layers; expect lower speed when offloading.

How much memory do I need for a 7B model?

Use the model card’s file size plus the context cache plus runtime overhead. The Step 1 calculator gives the minimum; do not rely on a single universal number.

Does llama.cpp support AMD Strix Halo?

Support depends on your build and backend. Follow the AMD guide linked in Requirements, then verify with a real model load and a GPU monitoring tool. If the run uses CPU only, the backend is not active.

🤖 AI summary

Updated: 2026-10-26
Topics: Strix Halo или RTX 4090, Strix Halo для ИИ, локальный LLM на AMD
Summary: Сравниваем производительность, VRAM и цену Strix Halo и RTX 4090 для запуска локальных языковых моделей и выбираем оптимальное железо.
Key facts:
- TL;DR Time: 60–90 minutes for one benchmark pass on each machine, plus model download time.
- Difficulty: Intermediate.
- You need: both platforms, one GGUF model file, one fixed prompt, llama-cli or another runner that reports tokens/s, nvidia-smi for RTX 4090, and the AMD monitoring tool documented for Strix Halo.
- Decision rule: choose the platform that fits your required model and context at an acceptable tokens/s rate; do not choose by brand name alone.
- Important: the source posts for this guide do not publish benchmark numbers or prices for these platforms.
- The numbers below are examples of the measurement format, not hardware claims.

AMD Strix Halo vs NVIDIA RTX 4090: What to Choose for Local LLMs in 2026

TL;DR

Requirements and setup

Step 1. Define the model memory budget

Step 2. Run the same benchmark on both platforms

Step 3. Choose the platform from your measurements

Troubleshooting

Summary and next steps

FAQ

Related guides

SteamOS or Windows 11 for Gaming and Work: Which to Choose

How to Run a Local LLM Server on AMD Strix Halo: HALOGEN and Qwen on Fedora 43

Best AI models and neural networks in 2026