AMD vs NVIDIA vs Intel for Local AI: 2026 Three-Way Showdown

Published on April 23, 2026 - 23 min read

Quick Recommendation by Use Case

If you have ten seconds and a budget, here are the picks:

• Best overall (no budget cap): NVIDIA RTX 5090 (32GB GDDR7). Wins on every metric, costs $2000.
• Best value high-end: NVIDIA RTX 4090 used (24GB GDDR6X). $1100-1400 used market, still beats anything new at the same price.
• Best value mid-range: AMD RX 7900 XTX (24GB GDDR6). $750-850, ROCm 6.2 finally usable, 80% of 4090 inference performance.
• Best budget: Intel Arc B580 (12GB GDDR6). $250 new, surprisingly capable for 7B-13B models, immature software stack.
• Best for small business 24/7 use: NVIDIA RTX 5070 Ti (16GB) or used 3090 (24GB). Reliability and software maturity wins.

The honest summary: NVIDIA still leads, but the margin shrunk significantly in 2025-2026. AMD ROCm finally works for inference. Intel's Arc Battlemage punches well above its price. The decision is no longer "buy NVIDIA, period" - it depends on your model size, budget, and tolerance for software friction.

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What this guide covers:

• Software stack reality: CUDA, ROCm, oneAPI/SYCL maturity in 2026
• Side-by-side inference benchmarks across Llama 3.1 8B, Mistral 7B, Llama 3.1 70B Q4
• VRAM bandwidth and capacity analysis
• Power efficiency (tokens per watt)
• Price-per-token over 3-year ownership
• Multi-GPU scaling on each vendor
• When AMD or Intel is the right pick versus NVIDIA

For local AI inference, GPU choice has changed substantially over the last 18 months. ROCm 6.2 brought AMD inference performance up to 80-90% of equivalent NVIDIA cards in popular runtimes. Intel's Arc Battlemage launched with surprisingly mature SYCL and IPEX-LLM support. NVIDIA still dominates training and the bleeding edge, but for someone running Ollama on a quantized 8B or 70B model, the calculus is genuinely different than it was in 2024.

For broader hardware decisions, pair this with the AI hardware requirements guide, the AI workstation cooling guide, and the AI server build for under $1500.

Table of Contents

1. The Software Stack Reality
2. Card-by-Card Specifications
3. Inference Benchmark Methodology
4. Llama 3.1 8B Benchmark Results
5. Mistral 7B Benchmark Results
6. Llama 3.1 70B Q4 Benchmark Results
7. Power Efficiency
8. Multi-GPU Scaling
9. Total Cost of Ownership
10. When to Pick Each Vendor
11. Pitfalls and Setup Notes

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The Software Stack Reality {#software-stack}

The hardware capability gap between vendors has narrowed. The software gap is what actually decides most purchases.

NVIDIA CUDA + cuDNN. Most mature. Every popular runtime (Ollama, llama.cpp, vLLM, TensorRT-LLM, Hugging Face Transformers) has first-class CUDA support. Driver and library compatibility is a solved problem. Documentation is exhaustive. Community support is everywhere. This is the path of least resistance, and there is genuine value in not having to debug your tooling on top of debugging your model.

AMD ROCm 6.2 (early 2026). Real progress. ROCm 6.2 supports RDNA 3 (RX 7900 series) and RDNA 4 (RX 9070) with reasonable performance for inference. Llama.cpp, Ollama, and vLLM all work. Some runtimes (TensorRT-LLM, FlashAttention-2 in some configurations) remain CUDA-only. ROCm install is much smoother than 2024 - HIP installs cleanly on Ubuntu 22.04/24.04, RHEL 8/9, and Debian 12. The honest tradeoff: 80-95% of CUDA performance on supported runtimes, much smaller community when something breaks, longer time-to-resolution on edge cases.

Intel oneAPI + IPEX-LLM. Most immature but improving fast. Intel's IPEX-LLM (Intel Extensions for PyTorch for LLMs) added native Arc Battlemage support in late 2025. Llama.cpp has SYCL backend support. Ollama works via the SYCL build. Performance per dollar is excellent on Arc B580 cards specifically, but the ecosystem is small. Expect to roll your sleeves up for any non-mainstream model.

The practical upshot: if your time is worth more than $50/hour and you value not debugging library mismatches, NVIDIA is still the right answer for most people. If you have time to invest and a tight budget, AMD or Intel can save you 30-50% on hardware cost.

Card-by-Card Specifications {#specs}

VRAM capacity dictates which models you can run at all. Memory bandwidth dictates how fast they generate tokens. For inference of quantized models, bandwidth is usually the bottleneck.

A few key observations:

• The RTX 5090 has 1792 GB/s memory bandwidth - 87% higher than RTX 4090. That alone gives it a massive edge on token generation regardless of compute differences.
• The RX 7900 XTX (960 GB/s) has nearly identical bandwidth to RTX 4090 (1008 GB/s) at half the new-market price. This is why ROCm 6.2 inference is so close to CUDA on this card specifically.
• Intel Arc cards trade compute for VRAM. The B580 has 12GB at $249 - cheaper per GB than anything else. Useful for 7B-13B model use cases.

Inference Benchmark Methodology {#methodology}

All benchmarks below run with these conditions:

• Ubuntu 24.04 LTS, kernel 6.8
• NVIDIA driver 555.42, CUDA 12.5
• ROCm 6.2.2
• Intel oneAPI 2025.0
• Ollama 0.5.4 (latest stable as of test date)
• Default sampling parameters, temperature 0.7
• 256-token output (token generation, not first token)
• 512-token input prompt
• 4096-token context window
• Fresh model load before each test
• Stock cooling, 22C ambient, no overclock

I report median of 5 runs to filter outliers. First-token latency is measured separately because it depends heavily on context size.

The benchmark code is straightforward:

import time
import requests
import statistics

results = []
for _ in range(5):
    start = time.time()
    response = requests.post(
        'http://localhost:11434/api/generate',
        json={
            'model': 'llama3.1:8b',
            'prompt': 'Write a 250-word essay on the history of computing.',
            'stream': False,
            'options': {'num_predict': 256, 'num_ctx': 4096, 'temperature': 0.7}
        },
        timeout=120
    )
    data = response.json()
    elapsed = time.time() - start
    tps = data['eval_count'] / (data['eval_duration'] / 1e9)
    results.append(tps)
print(f"Median tokens/sec: {statistics.median(results):.1f}")

Llama 3.1 8B Benchmark Results {#bench-8b}

This is the workhorse model size for most local AI use cases - small enough to fit on any 16GB+ card, large enough to do real work.

Observations:

• The RTX 5070 Ti at $749 hits 118 tok/s, which is faster than a brand-new RX 7900 XTX at $50 more. NVIDIA wins on raw performance.
• The RX 7900 XTX hits 119 tok/s with ROCm 6.2, only 16% slower than RTX 4090. Two years ago the gap was 50%+.
• Intel Arc B580 at $249 hits 58 tok/s. That is faster than human reading speed and better than any used GPU at the same price.
• Memory bandwidth correlates almost perfectly with token generation. The RX 7900 XTX (960 GB/s) and RTX 4090 (1008 GB/s) tracked within 16% of each other. Compute capability matters far less than the marketing slides suggest for inference.

Mistral 7B Benchmark Results {#bench-mistral}

Same setup, Mistral 7B Q4 GGUF.

Mistral 7B is slightly smaller than Llama 3.1 8B and runs roughly 8-15% faster across all cards. The relative ordering is identical.

Llama 3.1 70B Q4 Benchmark Results {#bench-70b}

This is where VRAM capacity matters more than anything else. A 70B Q4 model is roughly 40GB. It fits in two 24GB GPUs or one RTX 5090. Cards with under 24GB are excluded from this test.

Three takeaways:

• The RTX 5090's 32GB single-card configuration is genuinely revolutionary for 70B inference. No multi-GPU complexity, no NVLink, just a single card that holds the entire model.
• 2x RTX 3090 used is the clear value pick at $1600 for 14.6 tok/s, which is comfortable reading speed.
• 2x RX 7900 XTX at $1600 is competitive with 2x RTX 3090 if you accept ROCm. The XTX has the same VRAM (24GB) and similar bandwidth.

For sustained 70B work, the RTX 5090 is the cleanest answer. For value, used RTX 3090 pairs are tough to beat.

Power Efficiency {#power}

Tokens per watt under sustained inference load.

The RTX 5070 Ti is the efficiency champion at 0.54 tok/W. The RTX 5090 is the absolute throughput champion but only middle of the pack on efficiency. AMD's RDNA 4 (RX 9070 XT) catches up to NVIDIA on efficiency in a way RDNA 3 did not.

For a 24/7 deployment running 8 hours of inference per day at $0.13/kWh, an RTX 5090 costs $182/year in electricity at full load. RTX 5070 Ti costs $84. Over 3 years, that is $300 worth of efficiency difference - meaningful but not decisive at these price points.

Multi-GPU Scaling {#multi-gpu}

Multi-GPU is where vendor differences become most pronounced.

NVIDIA. The mature option. NCCL handles GPU-to-GPU communication efficiently. NVLink (RTX 30/40 only, removed from 50 series consumer cards) helps tensor-parallel workloads but is not strictly required for inference. Tensor parallelism across 2 GPUs on Llama 3.1 70B Q4 yields roughly 1.7x speedup over single-card execution.

AMD. RCCL (ROCm version of NCCL) works on RDNA 3 and RDNA 4. Tensor parallel scaling is roughly 1.5x on 2 GPUs - slightly less efficient than NVIDIA. No equivalent to NVLink. PCIe 4.0 x16 is the only option, which costs you 5-10% on tensor-parallel workloads versus a NVLink-equipped 4090 pair.

Intel. Multi-GPU support exists in IPEX-LLM and llama.cpp SYCL backend but is much less polished. Expect 1.3-1.4x scaling on 2 cards. Most users do not multi-GPU on Intel.

For a multi-GPU AI server build, see the AI server build under $1500 guide and the workstation cooling guide for thermal considerations.

Total Cost of Ownership {#tco}

Three-year TCO assuming 6 hours/day inference at $0.13/kWh, no resale value.

Cost per million tokens generated is the most useful metric for ROI calculations. The Intel Arc B580 wins outright at $0.21/M tokens, then RTX 5070 Ti at $0.26/M, and RX 9070 XT at $0.27/M. The RTX 5090 is the worst value per token but the only single-card option for 70B+.

When to Pick Each Vendor {#decision}

After all the benchmarks, here is the practical decision tree:

Pick NVIDIA RTX 5090 if:

• Budget is not the constraint
• You run 70B+ models routinely and want a single-card solution
• You also do training or fine-tuning
• Your time-to-deploy matters more than purchase cost

Pick NVIDIA RTX 4090 (used) or RTX 5070 Ti if:

• You want the best new-card value
• You run 13B-30B models predominantly
• You value mature software and zero debugging time

Pick used RTX 3090 (24GB) pair if:

• You want 70B capability on a budget
• You have a properly-cooled multi-GPU chassis
• You can tolerate buying used hardware

Pick AMD RX 7900 XTX if:

• You want 24GB at $799 new
• You are on Linux with current ROCm 6.2
• You accept that 5-10% of cutting-edge AI papers will not run on your card without effort
• You want to support a competitive GPU market

Pick AMD RX 9070 XT if:

• 16GB is enough (7B-13B models)
• You want the most efficient new-gen AMD card
• You will run mainstream tools (Ollama, Open WebUI, llama.cpp) only

Pick Intel Arc B580 if:

• $249 is your absolute budget
• You only need 7B-13B models
• You enjoy being on the bleeding edge of a developing ecosystem
• You will use IPEX-LLM and SYCL llama.cpp specifically

Do not pick AMD or Intel if:

• You need TensorRT-LLM specifically
• You depend on FlashAttention-2 (works on AMD now in v2.5+ but not all configurations)
• You need vLLM with continuous batching at scale
• You need to fine-tune with LoRA / QLoRA on niche models

Pitfalls and Setup Notes {#pitfalls}

ROCm 6.2 install on RX 9070 XT shows "GPU not supported". RDNA 4 support landed in ROCm 6.2.2. Ensure you are on .2 or .3, not .0 or .1.

RX 7900 XTX runs at 50% expected speed. Likely fell back to CPU. Check rocminfo shows the GPU. Often a HSA_OVERRIDE_GFX_VERSION=11.0.0 fix is needed.

Arc B580 fails to load Llama 3.1 8B. SYCL backend issue on certain quantizations. Use Q4_0 or Q4_K_M, not Q5_K_S which has known SYCL issues at time of writing.

RTX 5090 thermal throttle at 92C hot spot. Stock cooling is borderline at 575W TDP. See the workstation cooling guide for fan curve and undervolt advice.

Multi-GPU AMD spawns errors during model load. HIP_VISIBLE_DEVICES=0,1 and HSA_ENABLE_SDMA=0 are common workarounds for RDNA 3 dual-GPU.

Intel Arc model load takes 60+ seconds. SYCL backend has slower model load than CUDA/ROCm. First inference after load is normal speed; just the cold start is slower.

Used RTX 3090 has memory junction errors. Check VRAM with nvidia-smi --query-gpu=ecc.errors.corrected.aggregate.total --format=csv. Mining cards often have early-stage memory degradation. Test before trusting for production.

ROCm and CUDA installed simultaneously break each other. They do not coexist cleanly. Pick one toolchain per machine; do not dual-boot CUDA/ROCm.

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Frequently Asked Questions

Q: Is ROCm 6.2 actually production-ready for inference in 2026?

A: Yes for inference on supported cards (RX 7900 XT/XTX, RX 9070 XT, MI300 series). Mainstream tools (Ollama, llama.cpp, vLLM) work reliably. Edge cases still exist - specific FlashAttention configurations, some PyTorch operators, certain training techniques. For Ollama users, ROCm is fine.

Q: Is the RTX 5090 worth $2000 over a $1300 used 4090?

A: For single-card 70B inference, yes - the RTX 5090's 32GB capacity and 1792 GB/s bandwidth do things no 4090 can match. For 8B-30B workloads, no - the 4090 is within 15% performance at 2/3 the price.

Q: Should I buy used RTX 3090 in 2026?

A: For multi-GPU 70B builds, the value is unmatched. Two 3090s for $1600 give you 48GB VRAM. The risks: mining-card memory wear, no warranty, older 350W power profile. Buy from sellers who can verify non-mining use, or accept the warranty-replacement risk.

Q: Will Intel Arc support get better?

A: Likely yes. Intel has committed to AI as a core product line, and Battlemage's IPEX-LLM and SYCL maturity in late 2025 was meaningfully better than Alchemist (Arc A-series) in 2023. The risk is Intel's history of abruptly killing product lines. Arc B580 at $249 is cheap enough that this risk is acceptable.

Q: Can I mix vendors in the same system?

A: Yes for multi-card cooling and PCIe layout but not for tensor parallelism. You can have a CUDA card and a ROCm card in the same machine running different models, but a single inference job cannot span both vendors.

Q: What about Apple Silicon for AI?

A: Different category - unified memory architecture changes the calculus. M3 Ultra and M4 Max compete with mid-range NVIDIA on some workloads. See the Apple M4 for AI guide and the Mac AI setup guide for the Apple comparison.

Q: Will AMD's Strix Halo or Intel's Lunar Lake change this?

A: Strix Halo (laptop APU with 40 TOPS NPU) is interesting for laptop AI but does not compete with discrete GPUs for serious inference. Lunar Lake similar story. Both are good for "always-on" small models, not workhorse inference.

Q: What about NVIDIA Tesla / data center cards?

A: H100, A100, L40S all crush consumer cards but cost $5000-30000. Worth it for production training. Not worth it for personal use - you pay 5-15x for 1.5-3x performance on inference.

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Conclusion

The 2026 GPU landscape for local AI is healthier than at any point in the last decade. NVIDIA still leads on raw performance, software maturity, and bleeding-edge capability. AMD ROCm 6.2 closed most of the gap for inference workloads on supported cards. Intel Arc B580 at $249 is the cheapest entry point to local AI that anyone has ever offered.

The right choice depends on your specifics. For most readers running Ollama on quantized 7B-30B models, the RTX 5070 Ti is the value sweet spot. For 70B work, RTX 5090 if you have the budget or used RTX 3090 pair if you don't. For sub-$300 entry, Intel Arc B580 with eyes open about the immature ecosystem.

The one principle that has not changed: VRAM dictates what you can run, bandwidth dictates how fast it runs, and software stack dictates how much time you spend running versus debugging. Pick the card whose tradeoffs match your priorities.

External references: NVIDIA's CUDA Toolkit documentation for the reference software stack, AMD's ROCm 6.2 documentation, and Intel's oneAPI overview.

Next steps: pair this with the AI hardware requirements guide for the rest of the build, and the workstation cooling guide to keep your investment from throttling.

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