RTX 5090 vs Strix Halo 2026: Local LLM and CUDA Backtesting Benchmarks
The 2026 Dilemma: Speed or Memory?
You want to run LLMs locally. No more paid APIs, no more latency, no more censorship. Two options compete at the same price point (~$3,500):
- NVIDIA RTX 5090: 32 GB GDDR7, 1,792 GB/s bandwidth, 21,760 CUDA cores
- ASUS Strix Halo (Ryzen AI Max+ 395): 128 GB unified memory, 256 GB/s, integrated Radeon GPU
It's the Ferrari vs truck match: one goes fast but carries less, the other carries everything but slowly.
Head-to-Head Specs
| Spec | RTX 5090 | Strix Halo |
|---|---|---|
| Memory | 32 GB GDDR7 | 128 GB unified |
| Bandwidth | 1,792 GB/s | 256 GB/s |
| Ratio | 7x faster | 4x more memory |
| GPU | Blackwell (CUDA) | Radeon 8060S (ROCm) |
| Form factor | PCIe card (desktop) | Mini-PC / standalone |
| TDP | 575W | ~100W |
| Price | ~$3,500 | ~$3,500 |
| Ecosystem | CUDA (industry standard) | ROCm (catching up) |
What Models Run on What?
RTX 5090 (32 GB)
| Model | Quantization | VRAM | Est. Speed | Quality |
|---|---|---|---|---|
| Qwen 2.5 7B | Q8_0 | 8 GB | ~120 tok/s | Good |
| Qwen 2.5 14B | Q5_K_M | 11 GB | ~80 tok/s | Very good |
| Qwen 2.5 27B | Q5_K_M | 19 GB | ~50 tok/s | Excellent |
| Qwen 2.5 72B | Q3_K_S | 30 GB | ~20 tok/s | Degraded |
| Hermes 3 8B | Q8_0 | 9 GB | ~110 tok/s | Good |
| Llama 3.1 405B | - | - | ❌ Impossible | - |
Sweet spot: Qwen 2.5 27B Q5 — excellent quality, 50 tokens/second, 13 GB headroom for context.
Note, 10 October 2026: the Qwen 2.5 range comes in 14B and 32B, not 27B. The "27B" rows are estimates to revisit; they stay until measured.
Strix Halo (128 GB)
| Model | Quantization | RAM | Est. Speed | Quality |
|---|---|---|---|---|
| Qwen 2.5 72B | Q5_K_M | 50 GB | ~8 tok/s | Excellent |
| Llama 3.1 70B | Q5_K_M | 48 GB | ~8 tok/s | Excellent |
| Llama 3.1 405B | Q4_K_M | ~110 GB | ~3 tok/s | Top tier |
| DeepSeek R1 671B | Q2_K | ~120 GB | ~2 tok/s | Possible but slow |
Sweet spot: Llama 70B Q5 — top quality, but ~8 tokens/second (slow for production).
Real-World Case: Strategy Arena Lab
Strategy Arena Lab 0.1.0-241 is free: Windows (NVIDIA GPU optional) and Linux (CPU engine). That is where the hardware choice shows for trading research:
- Variant search. On Windows with an NVIDIA card, sweeping thousands of variants of a strategy runs on CUDA. Without NVIDIA, on a Strix Halo for instance, the same search runs on the CPU, more slowly; the CPU stays the reference engine that replays every ranking.
- Driving the Lab with a local model. The Lab connects to LM Studio or opencode through the MCP server. Memory decides which model fits, so the tables above apply as they are. The AI leaderboard has a "Local (personal GPU)" category, still empty on 10 October 2026.
- The judge does not depend on the hardware. Paste your Pine into the Lab, the judge replays it trade by trade under TradingView's rules (
tv_exact_v1, 234 of 234 trades identical in the real TradingView for the 6 leaderboard strategies), then export back to TradingView.
Multi-GPU: The Real Game Changer
The RTX 5090 combines with existing GPUs via Ollama's multi-GPU support:
| Config | Total VRAM | Best Model | Speed |
|---|---|---|---|
| 5090 alone | 32 GB | Qwen 27B Q5 | ~50 tok/s |
| 5090 + 4080 | 48 GB | Qwen 72B Q4 | ~30 tok/s |
| 5090 + 3090 | 56 GB | Qwen 72B Q5 | ~35 tok/s |
| 5090 + 4080 + 3090 | 72 GB | Qwen 72B Q8 (max quality) | ~25 tok/s |
With 48 GB (5090 + a 4080), you can run Qwen 72B, a 70B-class model, locally, with no API bill.
The Strix Halo's 128 GB is fixed. No expansion possible.
The Economics
If you're paying for AI APIs:
| API Spend | Per Month | Per Year |
|---|---|---|
| GPT-4o-mini (light) | ~$20 | $240 |
| Claude Haiku (production) | ~$50 | $600 |
| Multi-provider (6 AIs) | ~$100 | $1,200 |
An RTX 5090 at $3,500 pays for itself in under 2 years. And it runs 24/7 with zero rate limits.
On Strategy Arena, our Content Factory generates a daily article via API (~$0.02/day). With a local GPU: $0.00 — and the model is better because there's no rate limiting.
CUDA vs ROCm: Ecosystem Matters
- CUDA (NVIDIA): 95% of ML tools work natively. PyTorch, Ollama, vLLM, TensorRT — everything just works.
- ROCm (AMD/Strix Halo): Improving fast, but some tools aren't fully compatible yet. Ollama supports ROCm, but optimizations are less mature.
In Strategy Arena Lab, the GPU sweep is written in CUDA: it needs an NVIDIA card (Windows build). On a Strix Halo, the Lab runs on the CPU. On the arena side, our CUDA Evolved strategy was optimized on NVIDIA; on the 7 October 2026 reading it stood at −92.16% on paper: compute power does not protect against overfitting.
The Verdict
| Use Case | Winner | Why |
|---|---|---|
| Production AI (websites, APIs, agents) | RTX 5090 | Speed, CUDA, multi-GPU |
| Research (testing 405B, experimenting) | Strix Halo | 128 GB, giant models |
| Tight budget | RTX 3090 used (~$500) | 24 GB CUDA, unbeatable value |
| Gaming + AI combo | RTX 5090 | One card for everything |
| Silent / portable / low power | Strix Halo | 100W, mini-PC, silent |
For 90% of developers who want local LLMs to replace paid APIs: the RTX 5090 is the best investment in 2026.
For the 10% who absolutely need to test Llama 405B or DeepSeek R1 671B: the Strix Halo opens doors no discrete GPU can.
And for those starting on a budget: a used RTX 3090 at ~$500 with 24 GB runs Qwen 27B with no issues. Best entry point in 2026.
Explore Local AI on Strategy Arena
- Local GPU Models: Complete 2026 Guide
- CUDA and GPU Trading: How It Works
- Battle Royale: 6 AIs Trade Live
- Prompt Forge: 217 Tokens of Live Context
- AI Fear Index: Sentiment from 5 Intelligences
If you are comparing GPUs, test them on a trading workload, not only tokens/sec. Strategy Arena Lab turns supported Pine/ArenaScript runs into local CPU/CUDA proof.
Educational article by Strategy Arena. Benchmarks are estimates based on community tests and our own measurements. Prices are indicative (March 2026). Not purchase advice.
Also worth reading
🔗 NVIDIA DGX Spark: the full comparison for local AI — the third option next to the RTX 5090 and Strix Halo: NVIDIA's AI mini PC compared with the Mac Studio, GPU desktops and cloud GPUs (OutilsIA, in French).
Test your own strategy on data it has never seen: free Quick Check.
⚠️ Disclaimer — This article is for informational and educational purposes only. It does not constitute investment advice or a buy/sell recommendation. Past performance does not guarantee future results. Strategy Arena is an educational simulator with virtual capital. Always do your own research before making investment decisions.