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

> AI Release · @ai_release1 · https://ai-release.org/guides/amd-strix-halo-ili-nvidia-rtx-4090-chto-vybrat-dlya-lok_en.html

## 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- **Important:** the source posts for this guide do not publish benchmark numbers or prices for these platforms.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
![Illustration: Requirements and setup](https://ai-release.org/guides/img/amd-strix-halo-ili-nvidia-rtx-4090-chto-vybrat-dlya-lok-en-1.jpg?v=1792997341)

- 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:
```bash
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](https://ai-release.org/guides/kak-zapustit-lokalnyy-llm-server-na-amd-strix-halo-halo.html?utm_source=tg&utm_medium=channel&utm_campaign=guide_inline&utm_content=guide_to_guide). 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.
```bash
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:**
```text
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.
```bash
free -g
```
Expected on a Linux machine: a `Mem:` row with `total`, `used`, `free`, and `available` columns. On RTX 4090, also run:
```bash
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
![Illustration: Step 2. Run the same benchmark on both platforms](https://ai-release.org/guides/img/amd-strix-halo-ili-nvidia-rtx-4090-chto-vybrat-dlya-lok-en-2.jpg?v=1792997345)

**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.
```bash
/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:
```text
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](https://ai-release.org/guides/luchshie-nejroseti-i-modeli-2026.html?utm_source=tg&utm_medium=channel&utm_campaign=guide_inline&utm_content=guide_to_guide) before repeating this step.

## Step 3. Choose the platform from your measurements
![Illustration: Step 3. Choose the platform from your measurements](https://ai-release.org/guides/img/amd-strix-halo-ili-nvidia-rtx-4090-chto-vybrat-dlya-lok-en-3.jpg?v=1792997352)

**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:**
```bash
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.
```bash
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:
```bash
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:
```bash
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:
```bash
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.